Research: Science and Education edited by
Chemical Education Research
Diane M. Bunce The Catholic University of America Washington, DC 20064
Attitude Counts: Self-Concept and Success in General Chemistry
Christopher F. Bauer University of New Hampshire Durham, NH 03824
Scott E. Lewis,* Janet L. Shaw, and Judith O. Heitz Department of Chemistry and Biochemistry, Kennesaw State University, Kennesaw, GA 30144; *
[email protected] Gail H. Webster Department of Chemistry, Guilford College, Greensboro, NC 27410
General chemistry is a required first step for students who wish to pursue a career in science or health professions. The course also traditionally features low rates of student success and as a result serves as a gateway limiting access to science fields (1). This study seeks to better understand factors related to student success in general chemistry, using the human constructivism theory of Joseph Novak. As described by Bretz, the human constructivism theory details three domains of learning that must be addressed for meaningful learning to take place: cognitive, psychomotor, and affective (2). Meaningful learning is the process in which a learner relates new information to existing relevant information (3). The cognitive domain incorporates content knowledge and reasoning skills; its role in chemistry success has been well documented in the research literature. Past studies of college chemistry have shown a relationship between measures of general knowledge—such as SAT subscores (4–11), ACT scores (8, 12, 13) and high school grade point average (GPA) (12), as well as prior chemistry knowledge as measured by high school chemistry grade (6, 8, 14) and reasoning skills such as Piagetian tasks (4, 5, 7)—to performance in chemistry. The psychomotor domain relates to hands-on learning or the ability to actively manipulate physical materials to promote understanding. The psychomotor domain in the general chemistry setting is traditionally addressed in the chemistry laboratory setting. Research establishing the necessity of the psychomotor domain—as used through the lab—for meaningful learning is not extensive (15), however there is evidence that carefully crafted laboratory activities can improve student understanding (16, 17).
Nieswandt found that reported mid-semester self-concept was a stronger predictor of conceptual understanding than either students’ attitudes or interest in the subject. However, there is still the possibility that the role of self-concept in student performance may be a result of a spurious correlation to cognitive measures; students who enter the course with high self-concept may already be high achievers and are simply continuing to do so. This is of particular concern as studies have shown evidence that prior achievement affects students’ self-concept and attitudes toward science (20, 21). Given the established role of self-concept in students’ academic success, the first goal of this study is to describe the profile of students’ self-concept in undergraduate general chemistry. Doing so will provide an indication of the extent that low self-concept is present within this setting. The second goal of this study is to relate students’ self-concept to performance on an American Chemical Society (ACS) examination. Previous studies were primarily at the secondary level; the relationship between self-concept and chemistry performance has not been investigated at the postsecondary level. The third goal of this study is to determine whether self-concept plays a role in student performance after taking into account a cognitive measure. This would determine whether the affective domain represents a unique contribution to student performance beyond the cognitive domain as suggested by the human constructivism framework. Finally, this is a pilot study into the effect of a semester-long, active learning environment on improving selfconcept.
Self-Concept and the Affective Domain
Demographic Information The target population in this study was all of the students enrolled in first-semester general chemistry classes over the course of an academic year. This represents fourteen classes of general chemistry and approximately 780 students. Informed consent was requested of the students during the first week of class; 630 students consented to be part of the study. The SelfConcept Inventory (SCI) was administered after the first major examination in the course. This was done so that students were exposed to the expectations of the course. Such exposure would likely influence their self-concept regarding chemistry. Additionally, the SCI timing decision corresponds to Nieswandt’s
Recently, attention has been turned to the role of the affective domain in student success. In a 2005 article, Bauer highlighted multiple constructs that fit under the affective domain and determined self-concept to be both robust in measurement and relevant to educational research (18). Selfconcept is defined here as an evaluation one makes regarding one’s own ability and performance in a subject area. Supporting the claim that self-concept is a robust construct, Nieswandt measured ninth-grade students’ chemistry self-concept at the beginning and the middle of a semester and found the construct to be relatively stable (19). In addition,
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Methods
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Research: Science and Education Table 1. Comparison of Descriptive Statistics by Subscale on the Self-Concept Inventory Self-Concept Subscales
Meana
Standard Deviation
Skewnessb
Kurtosisc
Cronbach’s α Value
Mathematics
4.92
1.33
‒0.636
‒0.242
0.930
‒0.205
‒0.500
0.920
Chemistry
4.38
1.26
Academic
5.21
0.836
‒0.286
‒0.316
0.735
Creativity
4.57
1.25
‒0.290
‒0.420
0.649
5.52
0.890
‒0.512
‒0.168
0.795
Academic enjoyment aN
= 411.
bStandard
error for skewness is 0.120 for this sample.
administration at mid-semester, which was found to have a stronger relation to success than self-concept at the beginning of the course (19). The survey was administered during class, with multiple make-up opportunities for students who were not present in class. Students received a small amount of credit for completing the survey. This procedure resulted in complete SCI data for 414 students (65.7% of the consenting students) with likely reasons for missing data including incomplete or illegible survey responses (0.6%), students missing class on the day of the survey, or students withdrawing from the course. A test for outliers (discussed below) indicated three students as having extreme scores and these students were removed from further analyses based on concerns that keeping the students in might substantially affect the analysis results. The analyses presented here focus on the remaining 411 students. Demographic information and student SAT subscores were obtained from institutional records. Among the 411 students, 66.7% are female, 81.2% are Caucasian, 9.0% are Black, 4.8% are Asian, and 3.2% are Hispanic, with an average age of 20.1 years old. Of the 411 students, 323 had available SAT subscores, with an average score of 537 on the verbal portion and 547 on the mathematics portion. Instrument: Self-Concept Inventory Bauer developed and validated a 40-item, Likert-scale, Self-Concept Inventory designed for chemistry students (18). The instrument includes five distinct subscales: chemistry selfconcept, mathematics self-concept, academic self-concept, academic enjoyment self-concept and creativity self-concept. For each subscale students rate themselves on a level from 1 to 7. The instrument was construct validated through exploratory factor analysis and content validated by demonstrating an improved self-concept as time in chemistry increases. Scoring of the SCI followed suggestions by the author of the instrument (18). Each of the five subscales in the SCI has a set of distinct items that relate to it. To determine a student’s score on each of the five subscales, the average for that set of items was taken. Outliers were checked by looking for students that were more than three standard deviations from the sample mean on any one subscale. Three students were more than three standard deviations below the overall average, and they were removed from the further analysis. Descriptive statistics on the five subscales for the 411 students are presented in Table 1. Among the areas to note, each of the subscales has a negative skew, indicating a peak toward the high end of the scale, and a negative kurtosis indicating a flatter distribution than a normal
cStandard
error for kurtosis is 0.240 for this sample.
distribution. While relatively few of the normality tests were two standard errors from zero, it may be interesting to note that mathematics and academic enjoyment self-concept each have a distribution that favors the high end of the scale. The Cronbach’s α value indicates the consistency across the items that relate to each self-concept. The numbers found here are similar to the published results (18), so that the instrument appears to be reliable across different settings. Also comparing Cronbach’s α values to the previously published results, the average on each self-concept scale here appears to be slightly higher than those previously found with a general chemistry sample. This is likely an indication of the differences in setting; the settings differ in the type of institution and in geographic location. Instrument: ACS Exam The First-Term General Chemistry ACS Exam (22) was used as the final exam in all classes in this study. The ACS exam is externally constructed and nationally available through the ACS Examinations Institute. The exam consists of 70 items that a panel of instructors at this study’s setting deemed appropriate for the content of the course. It had a Cronbach’s α value of 0.855. Because the ACS exam served as the final exam in the course it was a mandatory requirement for completion of the course and is thus also used as a measure of course completion or course retention. Of the 411 students who completed the survey, 349 (84.9%) took the ACS exam with the most likely reason for not taking the exam being students not completing the course. A small, significant correlation (0.139) exists between taking the ACS exam and the score on the mathematics portion of the SAT. Thus students with higher mathematics SAT scores are more likely to take the ACS exam and the students who complete the course are not representative of those beginning the course. A similar result has been found by others (7). Students who took the ACS exam in this study had an average score of 50.2% and a standard deviation of 14.4%. Self-Concept Profile of Students Cluster analysis was undertaken using the five self-concept subscales to create a profile that describes the self-concept of students in the sample. Cluster analysis is an algorithmic classification scheme that calculates cluster centers in a manner that will produce the smallest distance between each data point and the closest cluster center. The algorithmic approach used was Ward’s method, with squared euclidean distance as
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Research: Science and Education Table 2. Descriptive Categories of Student Groups Determined by Cluster Analysis of Self-Concept (SC) Subscales from Table 1 Description Category
N (% of Total)
Mathematics SC
Chemistry SC
Academic SC
Creativity SC
Enjoyment SC
High Self-Concept
44 (10.7)
6.35
5.97
6.24
5.86
6.37
High Math and Chem
50 (12.1)
5.93
5.50
5.30
3.33
5.58
Average Self-Concept
129 (31.4)
5.44
4.03
5.02
4.86
5.24
Low Math
111 (27.0)
4.12
4.79
5.47
5.02
5.84
Low Self-Concept
77 (18.7)
3.74
2.78
4.51
3.48
5.02
Overall Sample
411 (100)
4.92
4.39
5.21
4.57
5.52
Table 3. Distribution of Retention and Performance Statistics Description Category
N, Initial Sample
N, Final Sample
Retention, %
ACS Average, %
High SelfConcept
44
40
90.9
57.7
High Math and Chem
50
48
96.0
56.0
Average
129
111
86.0
49.7
Low Math
111
93
83.8
47.4
Low SelfConcept
77
57
74.0
45.4
Overall sample
411
349
84.9
50.1
the measure of distance to the cluster center (23). Both decisions were oriented toward producing the most independent groups, that is, minimizing overlap between the groups. As a result, students with similar reported self-concept were grouped together within one cluster. The remaining parameter that we needed to specify prior to the cluster analysis was the number of cluster centers, or the number of student groups to be found. The number of clusters can be established based on theory or on empirical results. Without a theoretical justification, we chose an empirical rationale. The cluster analysis was begun with eight clusters. This resulted in eight groups of students and each was characterized by the group mean on each subscale. Then the cluster analysis with seven clusters was run. We determined whether two similar groups were combined or instead two distinct groups were combined. The analysis continued by decreasing the number of clusters until a distinct group was lost. We found that five clusters was the last analysis that resulted from combining similar groups—four clusters tended to eliminate a distinct group. The analysis that resulted in five clusters is presented in Table 2, which describes each student group and indicates its relative population. The description category characterizes each group as it compares to the overall sample along with its placement on the 1–7 scale. The characterization of the groups relies primarily on the chemistry and mathematics self-concepts as Marsh has shown that the relation between self-concept and student understanding is content specific (24).
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Only one group scored markedly low on the chemistry self-concept: the low self-concept group had an average of 2.78 on the 1–7 scale and comprised 18.7% of the sample. The next lowest group, average self-concept, represents the largest portion of students and scored above the mid-point of 4 on the 1–7 scale. Two groups—high self-concept and high math and chem—scored high on the chemistry self-concept; combined, these represent 22.8% of the sample. To put the values in context, Bauer (18) reported that the average chemistry self-concept for general chemistry students was 3.88, for peer leaders in chemistry 5.38, and for chemistry majors 5.86. In this sample, fewer than 20% are placed in a group that could be characterized as having a low chemistry self-concept. With mathematics self-concept, however, two groups had low averages. The low math group at 4.12 was similar to the low self-concept group at 3.74. While both these values are close to the mid-point of 4, they are notably lower than the overall sample average of 4.92 and Bauer’s report of general chemistry students at 4.48. Combined, these groups represent 45.7% of the sample. The same two groups that marked chemistry high also marked mathematics high, together comprising 22.8% of the sample with averages close to 6. A χ2 test indicates that course completion varies for the different student groups, [χ2 (4) = 13.398, p = 0.009], and by examining the retention percents, the high self-concept and high math and chem have higher than average retention percents while low self-concept has lower than average (Table 3). A similar pattern emerges for performance on the ACS exam, where the high self-concept and high math and chem groups scored the highest among the five groups and over 5% higher than the overall sample. The difference between groups is statistically significant using an ANOVA test [F (4, 344) = 7.678, p < 0.001]. A follow-up Tukey test for pairwise comparisons resulted in significant differences between two sets of groups, with the first set, high self-concept and high math and chem, outperforming the second set, low math and low self-concept. Possible explanations for the difference are numerous. First, it may be that high self-concept is directly related to higher performance, and thus efforts should be made to address the selfconcept of all students in the course. Second, it may be a result of differences in course retention among the groups, though this would rely on the low self-concept groups disproportionately having high-achieving students withdrawing from the course. While this is a possibility, it seems unlikely. No reason exists to think low-achieving students with low self-concept would
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Research: Science and Education Table 4. Comparison of Residual ACS Scores by Subscale on the Self-Concept Inventory Description Category
Residual ACS Scores, %
High Self-Concept
4.98
High Math and Chem
SAT Mathematics Scores
SAT Verbal Scores
572
542
2.81
578
548
Average
‒0.732
560
529
Low Math
‒1.17
532
551
Low Self-Concept
‒1.76
514
525
547
537
Overall Sample
0.141
disproportionately remain in the course. Finally, again, it may be possible that self-concept is related to a spurious variable, like high school preparation, that is ultimately responsible for their performance in the course. This possibility is investigated next. Role of Self-Concept when Controlling for High School Preparation To determine whether the relationship between selfconcept and student performance is spurious, SAT subscores were used as a proxy for high school preparation. SAT subscores have been shown to be reliable predictors of college success (25) and for success in general chemistry (1, 7). SAT subscores were chosen over high school chemistry grades, which vary greatly depending on the school and the level of chemistry taken. SAT subscores were also chosen over asking students to self-report their chemistry background, which may be unreliable. In order to look at the difference between the self-concept groups on the ACS exam while controlling for SAT mathematics and verbal scores, an ANCOVA analysis was run. The results of the ANCOVA indicate that SAT mathematics scores [F = 53.343, (1, 269) p < 0.001] and self-concept groups [F = 2.628 (4, 269), p = 0.035] had a significant relationship with ACS scores. Thus, student self-concept does appear to have a relationship to performance on the ACS exam while controlling for SAT subscores. To provide a qualitative description of the impact of self-concept, a regression analysis was run predicting ACS scores from the SAT subscores. The regression analysis resulted in eq 1. predicted SAT SAT = −1 18.346 + 0.106 math + 0.019 verbal ACS 2 R = 0.284
(1)
Using this equation, residual ACS scores were found by comparing the students’ actual ACS score to the predicted score, using eq 2. residual = actual − predicted (2) ACS ACS ACS Typically, residual ACS scores represent the error in the regression model, where some students overperform their predicted score and other students underperform. One hypothesis is that the affective domain, specifically student self-concept, can explain part of this error. The average residual ACS score for each self-concept group is presented in Table 4.
Residual ACS Scores for Self-Concept Groups The residual ACS scores suggest that part of the error in the regression model can be explained by student self-concept; on average the high self-concept students scored nearly 5% higher than their predicted ACS scores. To put this in context, the ACS exam in this study had a standard deviation of 14.4% and students in the high self-concept group scored over one-third of a standard deviation higher than their predicted score. Groups with lower self-concept also explain the residual in an expected fashion, with average residuals in the negative, indicating that they are underperforming their predicted scores. For example, low self-concept students scored 1.76% lower than their predicted scores. The difference in student group on this measure is statistically significant (F = 2.479, p = 0.044) corresponding to the results suggested with the ANCOVA analysis. The high selfconcept and low self-concept were the only pairs found to be significantly different on the Tukey follow-up test. This suggests that self-concept plays a role in addition to a cognitive factor, such as high school preparation as measured by SAT subscores. Another possible explanation is the role of the instructor, in that a particular instructor may positively or negatively affect both self-concept and student performance. This possibility seems unlikely given that instructional style was relatively uniform across the different classes sampled and no statistical difference was found in self-concept groups across the different instructors [χ2 (24) = 25.1, not statistically significant]. The role of self-concept is diminished after taking into account the cognitive domain. The high self-concept group scored 7.6% higher than the overall average (Table 3), but after controlling for SAT scores the group scored 4.8% higher (Table 4). Similarly, the low self-concept group scored 4.7% lower than the overall average, and, after controlling for self-concept, only 1.9% lower. This seems to indicate a degree of overlap between the cognitive and affective domain as indicated by these measures. Indeed, the five clusters differed statistically on SAT mathematics scores [F (4, 318)= 11.1, p < 0.001] with high self-concept related to higher SAT mathematics scores. For SAT verbal scores, though, [F (4, 318) = 1.94, not statistically significant] no significant relationship was found, and, curiously, the low math group scored the highest on this measure. This group—with high reading ability as measured by SAT verbal and low mathematics self-concept—would be an interesting subject of future study in particular in the conceptual versus algorithmic frameworks of chemistry (26).
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Research: Science and Education Table 5. Distribution of Pretest and Posttest Statistics Self-Concept Subscales
Pretest
Meansa
Posttest Meansa
Pooled Standard Deviation
t Value
p Valueb
Mathematics
4.73
4.73
0.56
0.00
—
Chemistry
4.56
4.80
0.70
3.05
0.003
Academic
5.36
5.38
0.54
0.37
—
Creativity
4.65
4.77
0.77
1.47
—
Academic Enjoyment
5.63
5.67
0.70
0.56
—
aN
= 82.
bThe
n.s. (—) indicates that the result was not significant at p < 0.05.
Pilot Study in Improving Students’ Self-Concept Given the importance of student self-concept demonstrated here, and elsewhere, a pilot study was undertaken to determine whether student self-concept could be notably improved within the course of a semester. The pilot study took place at a different setting, which was a small, liberal arts college located in the mid-Atlantic United States. At this institution general chemistry employs the process-oriented, guided-inquiry learning (POGIL) model that incorporates a pedagogy focusing on process skills and guided-inquiry activities (27). Previous research suggests that interventions designed to improve cognitive development may also assist improvement of students’ self-concept (28). The SCI survey was administered to students in four different classes of a first-semester general chemistry course after the first examination, and again at the end of the semester. After informed consent was requested, 100 students consented to be part of the study and complete pre–post data was available for 83 of the consenting students. An outlier test recommended the removal of one student’s data. The pre–post data regarding the remaining 82 students in the study is presented in Table 5. Using a paired-sample t test on each subscale we found that students’ chemistry self-concept improved during the course. The improvement, 0.24, is equivalent to an effect size (d ) of 0.477, which Cohen describes as a medium effect size (29). Examining the data further, of the 82 students there were 13 (16%) who improved on chemistry self-concept by at least one full point on the Likert scale and four students who decreased by at least one full point. The distribution of chemistry selfconcept scores at the beginning of the semester has four students scoring 6.50 or higher (out of seven), indicating a ceiling effect, preventing sizable gains, for a small group of students. The results do suggest that modest gains in student self-concept are possible within one semester when a student-centered pedagogy is used. The results also serve to call for future studies examining pre–post self-concept assessment within both traditional and alternative pedagogies, which could serve to put these results in context. Conclusions and Future Work The results of this study indicate that 18.7% of the sample belongs to the group identified as low self-concept, the only
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group featuring a markedly low chemistry self-concept. This percent indicates that a sizable, but not overwhelming, proportion of general chemistry students in the sample may be hindered by subject-specific low self-concept. In addition, the groups of selfconcept identified by the cluster analysis had wide discrepancies in retention rates (course completion) and in performance on an ACS exam. The ordering of the groups in terms of ACS exam scores indicated a clear relationship between higher self-concept and higher scores on the exam, as found elsewhere. Additionally, after controlling for SAT scores as a measure of the cognitive domain, self-concept continued to play a role in student performance on the outcome measure, indicating the affective domain does play a role that is separate from conventional cognitive measures. Finally, there is some evidence that chemistry self-concept can be improved over a semester-long course that employs an active learning pedagogy. The results of this study suggest several avenues for future research. Given evidence that the affective domain influences student performance, future studies may wish to investigate techniques to improve student self-concept and determine whether this ultimately improves students’ understanding. While this pilot study indicates that an active pedagogy can improve selfconcept over a small period of time, more work remains to be done. Another possibility for improvement of self-concept could be the incorporation of open-ended assessments. Because these assessments do not have one correct answer, they allow students to demonstrate their own understanding. Allowing and valuing, by assessing, this demonstration of understanding may improve self-concept. Finally, past studies have indicated that the role of self-concept differs based on student gender (21). This study did not have the statistical power to investigate gender-based differences, but preliminary results indicate the role of self-concept was different for male and female students. Literature Cited 1. Tai, R. H.; Sadler, P. M.; Loehr, J. F. J. Res. Sci. Teach. 2005, 42, 987–1012. 2. Bretz, S. L. J. Chem. Educ. 2001, 78, 1107. 3. Novak, J. D. A Theory of Education; Cornell University Press: Ithaca, NY, 1977; p 295. 4. Bender, D. S.; Milakofsky, L. J. Res. Sci. Teach. 1982, 19, 205–216. 5. Bunce, D. M.; Hutchinson, K. D. J. Chem. Educ. 1993, 70, 183–187.
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Research: Science and Education 6. Craney, C. L.; Armstrong, R. W. J. Chem. Educ. 1985, 62, 127–129. 7. Lewis, S. E.; Lewis, J. E. Chem. Educ. Res. Pract. 2007, 8 (1), 32–51. 8. Nordstrom, B. H. Predicting Performance in Freshman Chemistry. American Chemical Society National Meeting, Boston, MA, 1990. ERIC: ED347065. http://eric.ed.gov:80/ERICDocs/data/ ericdocs2sql/content_storage_01/0000019b/80/24/21/3a.pdf (accessed Mar 2009). 9. Pederson, L. G. Educ. Psych. Meas. 1975, 35, 509–511. 10. Pickering, M. J. Chem. Educ. 1975, 52, 512–514. 11. Spencer, H. E. J. Chem. Educ. 1996, 73, 1150–1153. 12. Carmichael, J. W. J.; Bauer, J. S.; Sevenair, J. P.; Hunter, J. T.; Gambrell, R. L. J. Chem. Educ. 1986, 63, 333–336. 13. House, J. D. Res. High. Educ. 1995, 36, 473–490. 14. Ozsogomonyan, A.; Loftus, D. J. Chem. Educ. 1979, 56, 173– 175. 15. Elliott, M. J.; Stewart, K. K.; Lagowski, J. J. J. Chem. Educ. 2008, 85, 145–149. 16. Rudd J. A. II.; Greenbowe, T. J.; Hand, B. M.; Legg, M. J. J. Chem. Educ. 2001, 78, 1680–1686. 17. Tien, L. T.; Teichert, M. A.; Rickey, D. J. Chem. Educ. 2007, 84, 175–181. 18. Bauer, C. F. J. Chem. Educ. 2005, 82, 1864–1870. 19. Nieswandt, M. J. Res. Sci. Teach. 2007, 44, 908–937. 20. Marsh, H. W.; Yeung, A. S. J. Educ. Psych. 1997, 89, 41–54.
21. Mattern, N.; Schau, C. J. Res. Sci. Teach. 2002, 39, 324–340. 22. Examinations Institute of the American Chemical Society, Division of Chemical Education. First-Term General Chemistry Exam; University of Wisconsin–Milwaukee: Milwaukee, WI, 2002. 23. Aldenderfer, M. S.; Blashfield, R. K. Cluster Analysis (Quantitative Analysis in the Social Sciences, number 44), Niemi, R. G., Ed.; Sage Publications: Newbury Park, CA, 1984. 24. Marsh, H. W. J. Educ. Psych. 1992, 84, 35–42. 25. Kobrin, J. L.; Patterson, B. F.; Shaw, E. J.; Mattern, K. D.; Sarbuti, S.M. Validity of the SAT for Predicting First-Year College Grade Point Average; The College Board: New York, 2008. 26. Nakhleh, M. B. J. Chem. Educ. 1993, 70, 52–55. 27. The POGIL Project Home Page. http://new.pogil.org/ (accessed Mar 2009). 28. O’Mara, A. J.; Marsh, H. W.; Craven, R. G.; Debus, R. L. Educ. Psychol. 2006, 41, 181–206. 29. Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; L. Erlbaum Associates: Hillsdale, NJ, 1988.
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