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3 PAIRWISE CORRELATIONS In its statement of task, the panel was asked to examine the correlations among a number of the variables in the Assessment (see Box 1-1). Several of the correlations are presented in this chapter, including correlations of student time to degree and completion rates with various characteristics of doctoral programs, and correlations between the diversity of a program’s faculty and the diversity of its students. All of the data are drawn from the tables of pairwise correlations found in Appendix D, in which any correlations greater than or equal to 0.31 are highlighted. The correlations provide insights into the relationships between characteristics that can be explored further. The panel focused its attention on correlation coefficients greater than or equal to 0.3 because they are nontrivial and they may display, in the panel’s view, important relationships between program characteristics. Pairwise correlations uncover these potential relations of interest. Where associations are detected that, based upon prior knowledge, are judged indicative of relationships worth further study, adjustments for potential confounding variables must be made. Such adjustments are beyond the scope of this brief report. Table 3-1 provides the correlations of student median time to degree and average cohort completion rate with three measures of faculty research productivity: average publications per faculty member, average citations per faculty member, and the percent of faculty with grants (see Appendix C for definitions). There is little relation between the average cohort completion rate and the productivity measures, with the exception of faculty with grants in physiology. The correlation of median time to degree and grants is also strong for physiology, and the correlations of median time to degree with citations per publication are strong for physiology, biomedical engineering and bioengineering, genetics and genomics, and immunology and infectious disease. Correlations in these four fields do not meet the 0.3 level with respect to publications per faculty, although they range from 0.179 to 0.272. The only field with a strong correlation between median time to degree and publications per faculty is nutrition. Where appreciable correlations exist between median time to degree and measures of faculty research productivity, greater research productivity is associated with longer times to degree. 1 Correlations of 0.295 and higher were rounded to 0.3. 17

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Table 3-1 Correlations of Median Time to Degree and Average Cohort Completion with Publications, Citations, and Grants Correlation with Median Time to Correlation with Average Degree Cohort Completion Percent Percent Faculty Average Faculty Average Pubs Average with Pubs Average with Fields per Faculty Cits/Pubs Grants per Fac Cits/Pubs Grants Biochemistry, Biophysics, and Structural Biology 0.052 0.166 0.077 0.123 0.089 0.094 Biomedical Engineering and Bioengineering 0.185 0.369 0.018 -0.184 0.015 0.148 Cell and Developmental Biology 0.014 0.128 0.081 0.087 0.057 -0.041 Genetics and Genomics 0.181 0.364 0.23 0.229 -0.02 0.149 Immunology and Infectious Disease 0.179 0.327 0.189 -0.067 -0.05 -0.02 Integrated Biological and Biomedical Sciences -0.12 0.058 0.04 0.056 0.021 0.014 Microbiology 0.232 0.289 0.302 -0.072 -0.087 -0.201 Neuroscience and Neurobiology 0.059 0.21 0.169 0.036 0.046 -0.03 Nutrition 0.475 0.216 0.202 -0.037 0.085 -0.095 Pharmacology, Toxicology, and Environmental Health -0.01 0.29 0.058 0.136 -0.095 0.117

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PAIRWISE CORRELATIONS 19 Table 3-2 correlates median time to degree and average completion rate with GRE General Test scores and the average number of Ph.D.’s in each program. The correlations between cohort completion and both average GRE and average PhDs are uniformly low, and in several fields are negative. The exception is physiology. There is a positive correlation with respect to median time to degree and both average GRE scores and average Ph.D.’s produced, but only in nutrition are both strongly correlated. In biomedical engineering and bioengineering there is a strong correlation between median time to degree and average number of Ph.D.’s, and in microbiology a strong correlation between median time to degree and average GRE scores. TABLE 3-2 Correlations of Median Time to Degree and Average Cohort Completion with GRE Scores and Number of PhDs Correlation with Correlation with Median Average Cohort Time to Degree Completion Average Ph.D.’s Average GRE 2002 to GRE Ph.D.’s 2002 Fields Average 2006 Average to 2006 Biochemistry, Biophysics, and Structural Biology 0.114 0.140 0.094 0.046 Biomedical Engineering and Bioengineering 0.251 0.491 0.080 -0.011 Cell and Developmental Biology 0.093 0.074 -0.022 -0.022 Genetics and Genomics 0.179 0.074 -0.108 0.235 Immunology and Infectious Disease 0.033 0.050 -0.216 0.051 Integrated Biological and Biomedical Sciences 0.111 0.145 -0.181 -0.033 Microbiology 0.319 0.270 -0.075 -0.089 Neuroscience and Neurobiology 0.156 0.150 0.007 0.076 Nutrition 0.487 0.309 -0.055 -0.106 Pharmacology, Toxicology, and Environmental Health 0.179 0.038 -0.058 0.103 Physiology 0.223 0.192 0.261 0.295 The correlations in Table 3-3 demonstrate a strong relationship between underrepresented minority faculty and underrepresented minority students in six of the eleven fields: Biochemistry, Biophysics, and Structural Biology; Immunology and Infectious Disease; Microbiology; Nutrition; Pharmacology, Toxicology, and Environmental Health; and Physiology. For a fuller discussion of underrepresentation see Chapter 5.

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20 RESEARCH-DOCTORATE PROGRAMS IN THE BIOMEDICAL SCIENCES The same relationship does not hold true for gender. The panel found no meaningful correlation between the percent of female faculty in a program and the percent of female students; the correlations are below 0.3 in every biomedical science field. The highest correlation (0.288) is in nutrition. While the average percentage of female students in all fields except biomedical engineering and bioengineering is over or near 50 percent, this is not the case with the average percentage of female faculty (see Appendix E). Only in nutrition is the average percentage of female faculty over 50 percent; the average percentage of female students is over 75 percent. Participation of women in faculty positions in the biomedical sciences is not a new issue. Women have consistently been represented on the faculty of biomedical fields at a rate lower than their proportion in the Ph.D. population.2 Thus, although programs with a higher percentage of minority faculty do indeed seem to attract minority students at a higher rate, the same is not true for women. TABLE 3-3 Correlations of Percent Female Students with Percent Female Faculty and Percent of Non-Asian Minority Students with Percent Minority Faculty Correlation with Correlation with Percent Female Percent Non-Asian Students Minority Students Percent Female Percent Minority Fields Faculty Faculty Biochemistry, Biophysics, and Structural Biology 0.170 0.489 Biomedical Engineering and Bioengineering 0.118 0.076 Cell and Developmental Biology 0.004 0.247 Genetics and Genomics 0.109 0.290 Immunology and Infectious Disease 0.014 0.150 Integrated Biological and Biomedical Sciences 0.227 0.529 Microbiology 0.233 0.765 Neuroscience and Neurobiology 0.204 -0.002 Nutrition 0.288 0.531 Pharmacology, Toxicology, and Environmental Health 0.187 0.370 Physiology 0.086 0.570 The correlations in Appendix D permit examination of many other relationships among the characteristics of doctoral programs, faculty, and students. For example, the relationship between program size (as measured by average number of Ph.D.’s) and research productivity (as measured by faculty publications, citations, and grant awards) may be of particular interest to some university administrators and researchers. Although correlation does not imply causation, 2 Research Training in the Biomedical, Behavioral, and Clinical Research Sciences, National Academies Press, 2011,p. 39.

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PAIRWISE CORRELATIONS 21 it would make sense that, in fields where laboratories are critical to research productivity, programs with larger laboratories would be more productive—even when measured on a per capita basis. This is seen in the relationship between the three measures of research productivity and number of Ph.D.’s, where several fields with higher values for these productivity variables also tend to have a larger number of Ph.D.’s (see Appendix E).

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