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Research: United States National Postdoc Survey results and the interaction of gender, career choice and mentor impact

  1. Sean C McConnell
  2. Erica L Westerman  Is a corresponding author
  3. Joseph F Pierre
  4. Erin J Heckler
  5. Nancy B Schwartz
  1. American Medical Association, United States
  2. University of Arkansas, United States
  3. University of Tennessee Health Science Center, United States
  4. Washington University in St. Louis, United States
  5. University of Chicago, United States
Feature Article
Cite this article as: eLife 2018;7:e40189 doi: 10.7554/eLife.40189
4 figures, 2 tables and 5 additional files

Figures

Demographics of the postdoc population surveyed.

(A) Postdoc gender; (B) Mentor gender; (C) Residency status; (D) Partnered/Married; (E) Has children; (F) Age; (G) Race/Ethnicity/Underrepresented status (which may include things other than race and ethnicity, such as LGBTQ or disability status); (H) Year of graduation; (I) Adjusted income, by year of graduation; (J) Postdoc satisfaction with mentor; (K) Primary long-term career plans; and (L) Primary field/discipline. White bars indicate female, black bars indicate male.

https://doi.org/10.7554/eLife.40189.002
Postdoc cost of living adjusted income and field of study by region.

(A) A map of the United States with the range of reported postdoc gross income adjusted by cost of living (key on the left). The adjusted income data are provided at the state (and when data sufficient to support, county) level. (B) The respondents’ field of study (key on the right) in each of the four major regions: West, Midwest, South, and Northeast (designated by bold lines on the map in A).

https://doi.org/10.7554/eLife.40189.003
Figure 3 with 1 supplement
Postdoc career choice.

Illustration of the independent effects of 10 of the 14 significant factors (out of 26) in the nominal logistic regression model of best fit for postdoc primary career choice (See Table 1 for effect statistics). A–C illustrate the effect of postdoc mentor and postdoc confidence on postdoc career choice; D–F illustrate the effect of postdoc productivity on postdoc career choice, and G–J illustrate the effect of demographics on postdoc career choice. In these mosaic plots, the panels show the listed factor and corresponding effect size, and the right-hand color key corresponds to primary career choice. Factors are paraphrased survey questions; please see Source Data 1 and Source Data 2 for specific wording of questions.

https://doi.org/10.7554/eLife.40189.004
Figure 3—figure supplement 1
Additional significant factors influencing career choice, but not depicted in Figure 3.

(A) Mentor rank; (B) whether the postdoctoral respondent was currently looking for a permanent position; (C) number of publications respondent had while a postdoc; (D) whether the postdoc respondent had received training in pedagogy. In these mosaic plots, the panels show the listed factor and corresponding effect size, and the right-hand color key corresponds to primary career choice. Factors are paraphrased survey questions; see Source Data 1 and Source Data 2 for specific wording of questions.

https://doi.org/10.7554/eLife.40189.005
Postdoc satisfaction with mentor.

Illustration of the independent effects of the eight significant factors (out of 26) in the nominal logistic regression model of best fit for satisfaction with postdoc mentor (see Table 2 for effect statistics). In these mosaic plots, the panels show the listed factor and corresponding effect size, and the right-hand color key corresponds to the degree of satisfaction with mentor. Factors are paraphrased survey questions; see Source Data 1 and Source Data 2 for specific wording of questions.

https://doi.org/10.7554/eLife.40189.008

Tables

Table 1
Significant factors influencing postdoc primary career plans.

A nominal logistic regression model that considered 26 factors that might be important for postdoc success and career choice revealed 14 significant factors. Factors are listed in order of decreasing effect size. Nominal logistic regression model, whole model effect: n = 6,504, Model R2 = 0.2017, AICc = 15924, BIC = 21130.

https://doi.org/10.7554/eLife.40189.006
Factorχ2-log p-value
Whether long-term career plans have changed599.951108.529
Received training in pedagogy151.05227.273
Feelings of career preparedness161.51011.925
Perceived mentor support of career plan130.57711.925
Primary field of study191.33110.190
Residency status in US133.2649.941
Job search intensity98.5749.352
Postdoc gender53.6547.658
Number of first, last, or corresponding author publications86.1935.274
Conferences attended in last year84.4685.043
Hours worked/week109.0934.870
Total number of publications while a postdoc80.5034.524
Academic rank of mentor70.5133.292
Plan to pursue a career in US37.4522.340
Table 2
Significant factors influencing postdoc satisfaction with mentoring. 

A nominal logistic regression model was calculated based on the same 26 factors used to model postdoc primary career plans (Table 1). Eight of these factors were found to be significant; factors are listed in order of decreasing effect size. Nominal logistic regression model, whole model effect: n = 6,504, Model R2 = 0.3007, AICc = 14729, BICc = 17810.

https://doi.org/10.7554/eLife.40189.007
Factorχ2-log p-value
Feelings of career preparedness960.457181.948
Perceived mentor support of career plan904.891178.146
Frequency of mentor meetings532.3189.480
Job search intensity68.2558.040
Received training in mentorship37.0886.240
Primary field of study92.1934.368
Perception of academic job market48.0883.384
Academic rank of mentor41.6142.508

Data availability

Non-privileged data used in this study are available in supplemental tables and additional material related to this manuscript. Due to their sensitive nature, much of the raw data are privileged to prevent individual identification, in accordance with IRB protocol. However, summary data for institutions, fields, and regions with more than 50 respondents are available upon request.

Additional files

Source data 1

Dataset 1: Pilot National Postdoc Survey used Feb 2–March 30, 2016

https://doi.org/10.7554/eLife.40189.009
Source data 2

Dataset 2: National Postdoc Survey used March 31–Sept 2, 2016

https://doi.org/10.7554/eLife.40189.010
Source data 3

Dataset 3: Changes between pilot and final survey

https://doi.org/10.7554/eLife.40189.011
Supplementary file 1

Tables S1 to S7.

Table S1. Population proportion analysis. Number of individual responses required from a total population of 100,000 for 95% and 99% confidence levels, at a 5% margin of error, assuming the true population proportion being measured is between 3% and 50% of the total population. Response values estimated using a population proportion analysis following equations and definitions described in Tintle et al. (Davis, 2009) and at Select Statistical Services Limited (Fleming et al., 2012). Table S2. National data summarized in main text and Figure 1. Table S3. Percentages of postdoc respondents in primary fields per US census region. There was a small, but significant, correlation between region and field (Pearson χ2, n = 7,585, χ2 = 134.145, p < 0.0001). Table S4. Nominal logistic model of gender disparity in pay. Gender remains a significant factor explaining postdoc salary, even when including year of terminal degree, age, partner status, parental status, type of institution, institution control, and satisfaction with mentor. Whole model n = 7,280, χ2 = 2589.077, p < 0.0001, AICc = 28458.9, BIC = 30056.2. Table S5. Gender salary disparity by field. Table S6. Respondent reported salaries adjusted to cost of living. Table S7. Factors included in nominal logistic regression models of satisfaction with mentor and primary career plan.

https://doi.org/10.7554/eLife.40189.012
Transparent reporting form
https://doi.org/10.7554/eLife.40189.013

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