When Advising Matters: Early Versus Late Faculty Advising and STEM Doctoral Students' Faculty Career Interests

When Advising Matters: Early Versus Late Faculty Advising and STEM Doctoral Students' Faculty Career Interests | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When Advising Matters: Early Versus Late Faculty Advising and STEM Doctoral Students' Faculty Career Interests Lechen Li, Jue Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9051283/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Doctoral advising is widely recognized as a key feature of graduate training that socializes students into research and academic career pathways. While advising is critical for career development, we know little about when advising matters the most across the doctoral trajectory. This longitudinal study investigated whether early versus late advising differentially predict faculty career interest among 330 biology doctoral students across 53 U.S. research universities. Results : Using exploratory factor analysis and block-wise ordinal regression, we found that early degree progress/feedback advising significantly predicted higher faculty career interest in year-4, even after controlling baseline intentions. By contrast, late advising factors added no incremental explanatory power once early experiences were accounted for. Conclusion : Findings suggest advising is most consequential when it provides structured progress feedback and scaffolds early scholarly production. Results support stage-based socialization theories and suggest STEM doctoral programs should embed career-relevant advising earlier. Doctoral education Advising Timing Career interest Socialization Introduction Each year, U.S. doctoral recipients disperse across a wide range of sectors, with the majority moving outside academic employment. In 2024, among Science and Engineering (S&E) doctorate recipients who had definite non-postdoc job commitments in the United States, 52.4% committed to industry/business while 30.1% committed to academia (National Center for Science and Engineering Statistics (NCSES), 2025 ). In several Science, Technology, Engineering, and Mathematics (STEM) fields, the academic share was even lower (e.g., Engineering (13.9%), Physical Science (17.9%)), indicating how commonly doctoral training culminates in non-faculty roles. Beyond this broad sectoral shift, faculty hiring remains sharply bottlenecked and prestige-stratified. Drawing on 2011–2020 academic employment from all U.S. PhD-granting universities, Wapman et al. ( 2022 ) find that a small fraction of institutions supply a disproportionate share of tenure-track faculty and hiring patterns reproduce status hierarchies across the system. Additionally, a recent report (National Academies of Sciences, Engineering, and Medicine, 2025 ) discusses the funding cuts and sociopolitical changes: grant cancellations, substantial National Institute of Health (NIH) and National Science Foundation (NSF) budget cuts, and tighter immigration policies constrict the pipeline for early-career researchers. Despite this diversification and challenges, doctoral culture often continues to signal faculty careers as the default metric of success. In shaping doctoral graduates’ career trajectories, advising matters in particular for the faculty pathway: not only do students gain stronger interest in research and academic self‑efficacy that help developing more optimism in academic career (Hu et al., 2026 ), many also perceive their advisors as more comfortable discussing and encouraging research-intensive academic paths (Sherman et al., 2021 ). On the other hand, program-level reforms with structured career exploration and advising are shown to improve career knowledge and decision confidence without increasing time-to-degree (Scalo & Freauff, 2020 ), suggesting the importance of intentional advising in broaden preparation. The empirical studies examining the association between advising and career trajectories have one persistent limitation: Most studies (e.g., Lopes et al., 2017 ; Sauermann & Roach, 2012 ) are “cross-sectional”, leaving unanswered when and how advising exerts the most leverage. This contrasts with stage-oriented socialization theory (Weidman et al., 2001 ) and others that view advising as dynamic across the doctoral journey. Beyond methodological limitations, higher education research rarely considers how the timing of support is organized by institutional expectations. Bennett and Burke ( 2018 ) argue that universities build informal timelines for when students are expected to develop, make decisions, and produce work, and that these expectations are shaped by unequal access to time, funding, and care resources. In doctoral education, such timelines are reflected in assumptions about when students should be developing as researchers, publishing, and preparing for the job market. This study aims to address that evidence-theory gap by conceptualizing advising timing and analyzing the relationship between multi-wave advising measures and students’ career interest. Data comes from a national, longitudinal sample of biology doctoral students across 53 research-intensive universities in U.S. Biology is a STEM discipline that not only shows relatively stronger faculty career interest among doctoral students than many other STEM fields (NCSES, 2025), but is also typically organized around laboratory apprenticeship, where career development is closely tied to advising and disciplinary socialization (Maher et al., 2020 ). We used factor analysis to interpret domains of advising practices and conducted ANOVA with Tukey test to compare students’ trajectories of faculty career interest. For inferential statistics, we estimated block-wise regression models on students’ faculty career interest in year-4. As doctoral education grows (Sarrico, 2022 ) and the academic labor market remains unsettled (Ganning, 2024 ), timing in advising takes on practical consequences. By tracing how advising unfolds across time rather than at a single snapshot, this study speaks to students, faculty, programs, and the wider research in doctoral education (Wood et al., 2020 ). Importantly, the aim of this study is not to steer students toward any single pathway but to align advising with diverse goals. Against the backdrop of a volatile academic market, such as constrained research budgets, uneven hiring, and intensified competition, this study offers actionable knowledge and implications for students, advisors, and programs seeking to make doctoral training responsive, equitable, and future-oriented. Literature Review Doctoral education is simultaneously a training pipeline for conducting research in both academic and professional field (Sarrico, 2022 ) and a socialization process that shape students’ professional identities (Gardner & Mendoze, 2023). Through research training and advisor-mentoring that shape skills, identities, and access to opportunities, they pursue roles across academia, industry, government, nonprofit sector, and students’ preferences may shift during training as they gain information, skills, and networks (Gibbs Jr et al., 2014 ; Roach & Sauermann, 2017 ). Still, both scholarship and policy tracking typically frame early outcomes through the faculty vs. non-faculty lens: national monitoring (e.g., the NSF Survey of Earned Doctorates) reports sectoral intentions and commitments in academic versus non-academic categories. A recent scoping review by Skakni et al. ( 2025 ) likewise map the literature around career choices/intentions and employment outcomes, and they highlight how “academic vs. beyond academia” remains the dominant way researchers describe doctoral careers. ​ Advising and doctoral career development Advisors play an instrumental role in shaping the professional and career trajectories of doctoral students by providing mentorship (Manson & Myers, 2012), socialization into academic and professional communities (Portnoi et al., 2015 ), and guidance through the complexities of research and career decision-making processes (German et al., 2019 ). In particular, they significantly influence students’ persistence toward degree completion (Paglis et al., 2006 ), career interest and readiness (Chang et al., 2021 ; Nersesian et al., 2019 ), thereby contribute broadly to the long-term work force development (De Welde & Laursen, 2008 ). Not all advising yields the same result on career outcome, and structured career advising remains insufficient within many doctoral programs (Austin, 2002 ). For instance, West et al. ( 2024 ) find that the majority students do not rely on their advisor during job search, regardless of the “type” of advisors. A practical distinction in “advisor type” is the difference between mentorship and sponsorships: Compared to a mentor, a sponsor not only “giving feedback and advice and uses his or her influence with senior executives to advocate for the mentee” (Ibarra et al., 2010 , p. 82). In a quantitative study, Pinheiro et al. ( 2017 ) examine patterns of doctoral publication, advisor advocacy, and subsequent scholarly productivity through the “sponsorships” lens. They find that students whose advisors actively promote their work through co-authorship and professional introductions are more successful in securing research positions. In their study, male students are more likely to benefit from such sponsorship behaviors, suggesting a potential gendered disparity in how advisors’ influence translated into visible academic capital. Complementing this, West et al. ( 2024 ) conduct a qualitative exploration of sponsorship in biological sciences doctoral programs in the US, examining how faculty advisors and other individuals shape students’ experiences during the job search process. Drawing on interviews with 47 doctoral students in biological sciences, they identify "sponsorship advisors" as those who “go beyond providing general support to leverage their personal networks to assist in the transition to full-time employment after graduation” (p. 381). For example, when a doctoral student (Jane) expressed interest in pursuing an industry career, her advisor provided sponsorship by acknowledging his own limitations and lack of expertise in that domain and introduced a secondary mentor who held a full-time position at a pharmaceutical company to bridge this gap. These studies suggest that effective advising on students’ career outcomes could be practice-specific, and uneven access to those practices may help explain disparities in outcomes (Charlesworth & Banaji, 2019 ). In addition to advising, prior studies also report several variables that predict doctoral students’ career development. For example, faculty career interests and outcomes are influenced by students’ demographics and backgrounds (e.g., Ginther, 2021 ; Roach & Sauermann, 2017 ; Tregellas et al., 2018 ), as these characteristics are often associated with differential access to resources, networks, and feelings of belonging in academia. Socialization with faculty is consistently viewed as key predictors of doctoral outcomes (Gardner, 2010; Flores-Scott and Nerad, 2012). Often broader than advising, faculty-socialization encompasses interactions that support academic development, professional identity formation, and career preparation (Zhao et al., 2007 ). On the other hand, while peer-socialization receives less attention in existing literature (Jeong et al., 2019 ), a few studies suggest that doctoral graduate interact with peers more frequently than with faculty (Weidman & Stein, 2003), and that this peer relationship can contribute to students’ experiences and outcomes (e.g., Dericks et al., 2019 ). Lastly, satisfaction with institutional service and resources, including perceived quality of career development, skills training, and overall doctoral experience, is positively related to confidence in achieving desired career outcomes (Lane et al., 2025 ). Yang and Cai’s ( 2022 ) study on doctoral student satisfaction also show that students who report stronger satisfaction with supervision, program structure, and research conditions tend to report higher interest in academic research careers. Timing and developmental nature of doctoral advising Theoretical literature often conceptualizes doctoral development as stage-based and interactive. In the socialization framework, for instance, Weidman et al. ( 2001 ) propose a dynamic, non-linear model in which doctoral socialization merges students’ prior inputs with faculty, peer, and community influences across personal, professional, and academic domains, emphasizing connectedness and networking that spans graduate school and postgraduation. Similarly, in the conceptual work by Baker and Pifer ( 2011 ), they outline three stages in doctoral education: (1) coursework/dependence, (2) transition toward independence (e.g., establishing writing routines, engaging in ongoing professional development), and (3) transition to scholar/long-term planning (e.g., developing a research agenda, receiving candid guidance about the academic profession and promotion/tenure). Because advising is one of the primary relational mechanisms through which programs support students’ progress and professional identity development, its content and consequences are likely to vary depending on when it occurs in the training trajectory. Pifer and Baker ( 2016 ) extend this idea by framing phases in doctoral training as knowledge consumption, knowledge creation, and knowledge enactment, arguing that each stage carries distinct advising functions: early navigational advising and legitimation of career exploration; mid-stage skill building via experiential opportunities and multiple mentoring sources; and late-stage sponsorship through introductions, recommendations, and advising for diverse careers, while also noting that challenges cluster both within stages and at the transitions between them. Advising timing, therefore, has become a theoretical expectation about when particular advising functions should matter most. Despite these stage-based expectations, the advising-careers literature remains predominantly cross-sectional, and they often examine career encouragement/support to contemporaneous intentions without taking time into consideration (e.g., Sauermann & Roach, 2012 ; St. Clair et al., 2017 ; Sherman et al., 2021 ; Cornér et al., 2017 ; Lopes et al., 2017 ). As an example, the study by Sauermann and Roach ( 2012 ) is based on a single 2010 survey of 4,109 doctoral students at 39 U.S. universities, and the “changes” in career preference were inferred by comparing cohorts and retrospective reports rather than following the same individuals over time. The authors explicitly note this limitation and call for “multiple real-time measurements” in future work. Taking together, existing research establishes advisors as essential to doctoral career development, identifies practices that improve advising quality, and shows that students’ intentions evolve across training. What remains underdeveloped is a time-sensitive account of when advising matters for which mechanisms and outcomes. To address this gap, we model advising as a stage-contingent, dynamic set of influences, particularly within the life sciences where academic labor markets are tight and career diversification is common (Gibbs et al., 2013; Wood et al., 2020 ). We ask two research questions: (a) how do year-1 advising and year-4 advising relate to year-4 faculty career interest? and (b) do associations of early and late advising with year-4 faculty career interest persist after controlling for starting plans (year-1 interest)? Method We analyzed de-identified secondary data from the Early Career Research (ECR) project (Feldon et al., 2023), a publicly archived dataset on the Open Science Framework ( https://osf.io/hymus/ ). ECR is a mixed-methods longitudinal panel that tracks the developmental trajectories of 336 biology doctoral students who began their doctoral studies in Fall 2014 across 53 research-intensive U.S. universities. Of the 336 participants, 132 (~ 40%) are men and 200 (~ 60%) are women, with four didn’t respond. 66 (19.6%) participants are international students. Racial and ethnic identification was predominantly White (n = 200; ~60%), followed by Asian (n = 71; ~21%), Black or African American (n = 21; ~6%)), and Hispanic or Latino (n = 26; ~8%), and multiracial or other (n = 18; ~5%). To reduce sparseness in models, we collapsed race to White, Asian, and Minority (combining Black, Hispanic/Latine, Native American, Pacific Islander, Other, and Multiple) in the current study, an approach previously applied to this dataset by Jeong et al. ( 2019 ). Each year, students complete an end-of-year survey capturing demographics, degree-progress indicators, perceptions of program quality and climate, socialization, research and publication activity, graduate advising, and related experiences. The dataset has supported prior quantitative and qualitative studies of doctoral development (e.g., Feldon, 2016; Roksa et al., 2018; Jeong et al., 2020; Zhang et al., 2022). In the current study, we drew explicitly from the first to fourth waves of the annual survey: this window provides sufficient time to observe within-person change for longitudinal analysis, while preserving a relative complete panel (later waves contain substantially more missingness due to attrition). We used multiple imputations by chained equations with a rich predictor set that included prior outcomes. Missing data was handled using multiple imputations by chained equations (MICE). The overall missing rate was 15.97% across the unimputed dataset, and the missing rate of key dependent variable (year-4 faculty interest) is 25.36%. In their methodological guidance on multiple imputation, Austin et al. ( 2021 ) proposed that “multiple imputation is blind to which variables are outcomes and which variables are predictors” (p. 1326). Therefore, we imputed all variables from the analysis model. Variables were specified according to measurement level (e.g., ordinal items coded 1–3, binary indicators coded 0/1, and year of birth treated as continuous). Little’s MCAR test for the CI outcome block was nonsignificant, χ²(425) = 422, p = .533, providing no evidence against MCAR for these outcomes. We generated 40 imputed datasets (m = 40) using 10 iterations (maxit = 10) with a fixed random seed to improve precision, in line with recommendations to use a relatively large number of imputations when the fraction of missing information is moderate (Bodner, 2008 ; Graham et al., 2007 ). Imputation models were specified by variable type and used an automatically constructed predictor matrix (quickpred; minpuc = .60), excluding participant identifiers and removing constant terms. Sensitivity analyses comparing pooled MI results with complete-case estimates yielded substantively similar conclusions. Variables Dependent variables . Because the outcome is explicitly about “faculty-track” interest, the dependent variable is students’ year-4 faculty career interest (currently, are you interested in or want to pursue each of these career options?- To become a professor in a college or university), measured on a 3-point ordered scale (1 = Not at all, 2 = Possibly, 3 = Definitely). We also captured their faculty career interests at the beginning of their programs to construct individual interest trajectories across time. Independent variables. The central independent variables are advising practices. Students complete a set of advising items (e.g., My primary advisor treats me with respect; My primary advisor provides information about career paths open to me) each year, rated on a three-point Likert scale (1 = Disagree, 2 = Neutral, 3 = Agree). We conducted exploratory factor analyses for the year-1 and year-4 advising item blocks using the completed imputed dataset. Because the indicators were ordinal, we estimated the factor model using a polychoric correlation matrix. Parallel analysis indicated five factors in each wave, and we applied oblimin rotation (Jennrich & Sampson, 1966 ) because the advising dimensions were expected to be correlated (Noe, 1988 ). Factor scores were computed using the Ten Berge method (Ten Berge et al., 1999 ) implemented in the “psych” package in R, which preserves the correlations among obliquely rotated factors and were centered at zero by construction (so the mean of each factor score are approximately 0). We then treated those factor scores as continuous measures of the underlying advising dimensions and used them as predictors in subsequent regression models. Control variables . We first controlled baseline demographics, including age, gender (0 = Male, 1 = Female), race, and citizenship (0 = Citizen, 1 = International). To reduce sparseness in models, we collapsed race to White, Asian, and Minority (combining Black, Hispanic/Latine, Native American, Pacific Islander, Other, and Multiple), an approach previously applied to this dataset by Jeong et al. ( 2019 ). We also adjusted for faculty/peer socialization and scholarly productivity in “early” and “late forms. Each year, students reported whether they engage with faculty and peers in four interaction types: social matters, field-related topics, intellectual interests and personal matters, using binary responses (0 = no, 1 = yes). We summed these four items within each year to create a socialization count, then averaged the year-specific counts across years 1–2 and years 3–4 to form early/late faculty socialization and early/late peer socialization, each ranging from 0 to 4. Similarly, conference presentations and publications were coded as two-year sums (year 1–2, year 3–4), producing early/late conference and early/late publication, all ranging from 0 to 2. This separation allowed us test whether early and late experiences differentially align with later faculty interest. Although we lacked university identifiers to cluster by institution, this sample is predominantly research-intensive, which likely reduces between-institution heterogeneity relative to mixed-sector samples. We also included composites of institutional resources (early/late satisfaction with services and facilities), using similar approaches, as proxies for campus context and conducted a design-effect sensitivity (Kish, 1965 ) that inflated standard errors under plausible within-university similarity. Results were substantively unchanged. Table 1 provides descriptive statistics for the dependent and independent variables. Table 1 Descriptive statistics of ECR doctoral respondents (n = 330) Variables Mean / Percentage SD Min Max Faculty career interest Year-1 faculty interest 2.20 0.76 1 3 Year-4 faculty interest 1.93 0.72 1 3 Advising factors Year-1 psychosocial support 0 1 -5.59 1.15 Year-1 network 0 1 -3.58 1.80 Year-1 reputation 0 1 -3.18 1.70 Year-1 degree/progress feedback 0 1 -3.34 2.04 Year-1 publication/ funding support 0 1 -4.37 2.16 Year-4 psychosocial support 0 1 -4.63 1.05 Year-4 reputation 0 1 -4.31 1.51 Year-4 network 0 1 -3.40 1.24 Year-4 publication, degree progress 0 1 -3.96 1.89 Year-4 early grad school transition 0 1 -3.96 1.78 Demographic Age 28.95 3.69 25 57 Gender (reference group: male) female 60% 0.49 / / Race (reference group: White) Asian 21% 0.341 / / Minority 29% 0.277 / / Citizenship (reference group: citizen) International 20% 0.40 / / Doctoral experience Early socialization with faculty 2.67 1.02 0 4 Late socialization with faculty 2.83 0.92 0 4 Early socialization with peers 3.62 0.67 0 4 Late socialization with peers 3.75 0.55 0 4 Early satisfaction with institution’s service and facilities 2.76 0.35 1 3 Late satisfaction with institution’s service and facilities 2.68 0.44 1 3 Early conference 0.08 0.32 0 2 Late conference 0.15 0.39 0 2 Early publication 0.51 0.69 0 2 Late publication 0.87 0.76 0 2 Inferential statistics: Block-wise ordinal regression To examine when and which advising dimensions predict later faculty interest while adjusting for confounds, we estimated cumulative-logit ordinal models with proportional odds and entered predictors in blocks. The first block included all control variables, including baseline demographics, early/late measures of socialization with faculty and peers, scholarly production, and satisfaction with institutional service and facilities. We then added the early advising factors (year-1) to assess whether early advising has durable associations with year-4 interest net of those controls. Next, we introduced the late advising factors (year-4) to test whether end-stage advising explains additional variance once early advising is held constant. Finally, we included baseline year-1 faculty interest to evaluate whether any advising effects persist beyond students’ initial intentions. We examined the proportional-odds assumption using threshold-specific coefficients and heterogeneous residual scale and found no evidence against the parallel-lines assumption, suggesting the ordinal logit specification is appropriate. Additionally, we assessed likelihood-ratio tests to identify whether each added block of predictors provides meaningful extra explanatory power to the previous model. Limitation First, the study draws on a single discipline, biology, and a relatively small cohort (N = 336) concentrated in research-intensive universities, meaning the estimates may not generalize to other fields or institutional sectors. Second, advising measures and outcomes are self-reports collected at year’s end. Common-method variance (Podsakoff et al., 2003 ) and recall bias (Sedgwick, 2012 ) are possible, and perceptions of advising may be intertwined with broader program climate. Third, our findings identify correlational relationships rather than causal effects. Future research may use different data with institution identifiers to estimate cluster-robust or quasi-experimental approaches. Nevertheless, this study advances existing literature by introducing an intuitive, developmentally grounded timing perspective that separates early and late advising and traces their associations to faculty career interest over time. Because few prior studies have leveraged longitudinal designs to interrogate when advising matters, these results provide a novel framework and generate testable hypotheses for subsequent research using larger, more diverse samples and more rigorous identification strategies. Findings First, we report the year-specific exploratory factor analyses that recover core dimensions of advising (with exemplary items in Table 2 and full items in Appendix I). Then, we present regression models of year-4 faculty interest, adding predictors in blocks to assess incremental explanatory power. Table 2 Factor analysis results (exemplary items only; see full result in Appendix I) Psycho-social support Year-1 Year-4 My primary advisor treats me with respect; (0.68) My primary advisor cares about me as a person (0.79) Psycho-social support My primary advisor treats me with respect; (0.57) My primary advisor cares about me as a person. (0.96) Network My primary advisor helps me get to know other faculty members at the University; (0.65) My primary advisor helps me get to know other faculty or professionals in my field. (0.79) Network My primary advisor helps me get to know other faculty or professionals in my field; (0.75) My primary advisor provides information about career paths open to me. (0.31) Capital/prestige/reputation My primary advisor was recommended to me by other people; (0.45) My primary advisor is well known in my discipline or field of study. (0.82) Capital/prestige/reputation My primary advisor was recommended to me by other people; (0.32) My primary advisor has a reputation for being a good advisor to graduate students. (0.57) Degree/progress feedback My primary advisor gives me constructive feedback on my progress toward degree completion; (0.50) My primary advisor provides information about career paths open to me. (0.38) Degree/progress feedback, publication and funding support My primary advisor gives me constructive feedback on my progress toward degree completion; (0.55) My primary advisor initiates discussions about the progress I am making toward my degree; (0.55) My primary advisor encourages me to publish; (0.37) Research/funding support My primary advisor encourages me to publish; (0.55) My primary advisor helps me secure funding for my graduate studies. (0.69) Graduate school transition in year-1 When I first came to my university, my primary advisor helped me with the transition to graduate school. (0.31) Notes. Values in parentheses are standardized factor loadings. Items are assigned to factors based on their largest absolute loading Factor analysis: stable advising domains over time with modest developmental shifts Exploratory factor analyses were conducted separately for year-1 and year-4 advising items. A five-factor solution was retained in each wave, and factors were labeled based on the highest-loading items (see Table 2 ). For year-1 advising items, five conceptually stable domains emerged (see Table 2 ): Psychosocial Support (e.g., advisor cares, respects, is available to talk when needed), Networking (introductions to faculty/professionals; navigating departmental politics), Degree Progress / Feedback (initiates progress conversations; constructive feedback; degree requirements), Advisor Capital / Prestige (advisor reputation, funding ability, timely completion), and Publication / Funding Support (encouragement to publish; funding help). At year-4, the EFA produced a similar set of advising domains, but with one modest reconfiguration in ways that are substantively consistent with developmental changes in doctoral training: the item “When I first came to my university, my primary advisor helped me with the transition to graduate school” formed a standalone factor rather than loading with other year-4 advising items. This is unsurprising, given the item is retrospective that explicitly asks respondents to evaluate an advisor behavior tied to program entry rather than the current advising relationship. Notably, the item “my advisor provides information about career paths open to me” loaded with degree-progress in year-1 but with networking in year-4. This pattern aligns with developmental models of doctoral socialization in which early advising centers on adjustment to program structures and degree progress, while later advising emphasizes professional integration and external networks. In particular, the socialization theory (Weidman et al., 2001 ) and Gardner’s ( 2009 ) phase-based framework both anticipate a shift from progress/feedback to outward-facing professional socialization across the doctorate. Studies of advisor influence on evolving career intentions similarly indicate that career guidance becomes more tied to networking and labor-market navigation (e.g., Gibbs & Griffin, 2013 ; Sauermann & Roach, 2012 ; St. Clair et al., 2017 ). Additionally, the item capturing advisors’ encouragement of publication exhibited a shift in its factor category from year-1 to year-4. We interpret this movement as substantively consistent with the developmental organization of doctoral training. Early in the program, “encourages me to publish” may function as an initial push toward research productivity. By year-4, however, publication may become closely tied to concrete progress toward finishing the doctoral study and preparing for the job market, instead of simply “encouragement”. Inferential: advising timing and faculty career interest Table 3 presents the summary of our block-wise ordinal logistic regression models. Recalling our modeling strategy, we estimated cumulative-logit models using block-wise incremental adjustment. In Model 1, the first block entered demographics and broad, non-advising program composites to account for experiences plausibly correlated with both advising and career interest. Model 2 included the second block, early (year-1) advising domains, to test whether early practices explain variance above and beyond those program factors. Model 3 added the third block, late (year-4) advising domains, to assess any incremental contribution of end-stage advising once early advising and program experiences are held constant. Model 4 included baseline year-1 faculty interest so that any remaining advising associations reflect changes or persistence beyond students’ initial plans. This sequence lets us isolate whether advising—especially early advising—adds explanatory power over and above the broader doctoral context. Table 3 Summary of block-wise regression models Variables Model 1 Model 2 Model 3 Model 4 OR SE OR SE OR SE OR SE Early Advising Y1 psychosocial support 1.042 0.133 0.952 0.138 0.92 0.139 Y1 network 0.861 0.125 0.818 0.128 0.843 0.13 Y1 reputation 1.043 0.111 1.05 0.117 1.109 0.12 Y1 degree/progress feedback 1.532*** 0.123 1.486** 0.124 1.488** 0.127 Y1 publication/ funding support 1.08 0.12 1.075 0.121 1.137 0.124 Late Advising Y4 psychosocial support 1.056 0.131 1.051 0.134 Y4 reputation 0.984 0.126 0.97 0.127 Y4 network 1.126 0.13 1.006 0.134 Y4 publication, degree progress 1.184 0.125 1.23 0.129 Y4 early grad school transition 1.185 0.119 1.164 0.119 Baseline interest Y1 faculty interest 2.819*** 0.223 Demographics Age 1.012 0.03 1.007 0.031 1.01 0.031 1.028 0.031 Gender (reference group: male) female 0.843 0.222 0.799 0.225 0.808 0.228 0.928 0.232 Race (reference group: White) Minority 1.12 0.277 0.989 0.281 1.011 0.284 0.955 0.289 Asian 0.992 0.341 0.945 0.347 0.941 0.348 0.885 0.352 Citizenship (reference group: citizen) International 1.51 0.338 1.587 0.342 1.469 0.346 1.315 0.352 Doctoral experiences Early socialization with faculty 0.976 0.144 0.901 0.151 0.914 0.152 0.905 0.153 Late socialization with faculty 1.455* 0.152 1.459* 0.155 1.380* 0.159 1.359 0.160 Early socialization with peers 0.797 0.207 0.816 0.210 0.793 0.214 0.729 0.219 Late socialization with peers 0.970 0.238 1.064 0.243 1.117 0.248 1.019 0.252 Early satisfaction with institution’s service and facilities 0.701 0.355 0.669 0.365 0.658 0.367 0.707 0.372 Late satisfaction with institution’s service and facilities 1.077 0.285 1.018 0.29 0.96 0.298 1.09 0.301 Early conference 0.737 0.366 0.738 0.369 0.741 0.377 0.686 0.379 Late conference 1.202 0.288 1.057 0.294 1.085 0.297 1.16 0.303 Early publication 1.455* 0.163 1.544** 0.168 1.583** 0.171 1.535* 0.172 Late publication 1.073 0.143 1.161 0.147 1.097 0.149 1.141 0.151 Notes: Significance: *** p<.001, ** p<.01, * p<.05; Y1 = year-1, Y4 = year-4. RQ1: how do early/late advising relates to students’ year-4 faculty career interests Model 1 indicated that, holding other variables constant, late socialization with faculty (OR = 1.46, p < 0.05) was positively associated with Year-4 faculty career interest, meaning that a one-unit increase in late faculty socialization was associated with 46% higher odds of reporting higher year-4 faculty-interest category. This pattern is consistent with prior studies suggesting that advisors are viewed as more comfortable discussing research positions and as preferring research roles for their advisees (Sherman et al., 2021 ). Early publication activity (OR = 1.46, p < 0.01) was also positively associated with increasing year-4 faculty interest. suggesting a 46% increase in the odds of being in a higher interest category. Adding early advising (year-1) domains in Model 2 significantly improved model fit (Δ–2LL = 12.0, p < 0.05). In this model, year-1 degree progress/feedback advising emerged as a unique positive predictor (OR = 1.53, p < 0.001), meaning that a one-unit increase in this scale was associated with 1.53 times the odds of being in a higher category of year-4 faculty career interest. By contrast, the effect of late socialization with faculty (OR = 1.46, p < 0.05) and early publication (OR = 1.54, p < 0.01) remained robust. In Model 3, adding the Year-4 advising factors did not significantly improved fit over Model 2 (Δ–2LL = 10.5, p ≈ .06). Additionally, no late advising factor showed a significant association once early advising and other experiences were controlled. In comparison, late socialization with faculty (OR = 1.38, p < 0.05), early publication (OR = 1.58, p < 0.01), and year-1 degree progress/feedback advising (OR = 1.49, p < 0.01) continued to be positively associated, and the effect sizes remained similar. RQ2: Year-4 faculty career interest change: controlling for starting plans Model 4 added baseline year-1 faculty career interest, which substantially improved fit (Δ–2LL = 34.5, p < .001). It was positively correlated (OR = 2.82, p < 0.001) with year-4 faculty career interest. This means that a one-unit increase in baseline year-1 faculty career interest is associated with 2.82 times higher odds of reporting a higher level of Year-4 faculty career interest. In addition, even conditioning on initial intentions, early publication (OR = 1.54, p < 0.05) and early progress/feedback advising (OR = 1.48, p < 0.01) remained significant, which were equivalent to around 54% and 48% higher odds of being in a higher year-4 interest category per one-unit increase. This pattern indicates that these two early experiences were associated with gains or persistence in faculty interest over time, not merely cross-sectional differences among students who already intended an academic career. However, late socialization with faculty became insignificant, and none of the late advising domains were significant. Summary of findings Across the models, the results point to a clear timing pattern: early experiences appear to matter more than late experiences in predicting year-4 faculty career interest, at least in this dataset. In particular, early degree progress/feedback advising and early publication remained positive and statistically significant predictors even after adjusting for a broad set of covariates and baseline year-1 faculty career interest. By contrast, adding year-4 (late) advising factors did not yield meaningful incremental explanatory power and none of the late advising domains showed an independent association once early advising and prior experiences were accounted for. Additionally, the initially positive association between late faculty socialization and year-4 faculty career interest became non-significant after conditioning on baseline interest. Consistent with prior studies (Lopes et al., 2017 ; St. Clair et al., 2017 ), this finding suggests that later socialization may reflect continuation of pre-existing orientations—students who already had higher faculty career interest early on may be more likely to seek out to their advisors—rather than serving as a distinct driver of changes in career interest. Discussion STEM doctoral advising is widely recognized as a key feature of graduate training that socialize students into research and academic career pathways (Austin, 2022). Prior work has shown that effective advising encompasses multiple functions (e.g., Paglis et al., 2006 ; Zhao et al., 2007 ), yet the literature is less clear on whether their influence is timing-specific. Developmental perspectives (e.g., Bennett & Burke, 2018 ; Weidman et al. ( 2001 ) suggest that early advising may be particularly consequential because it establishes expectations, routines, and a sense of feasibility during a period of heightened uncertainty, whereas later advising may be less differentiating once trajectories have already been set. Empirically, however, few studies have measured advising multidimensionally at multiple time points and directly tested whether the timing of advising matters for subsequent academic career interest. In this study, we recovered the multidimensional structure of doctoral advising at multiple time points and test whether the timing of advising—early versus late—matters for year-4 intentions after accounting for demographics, program context, and baseline interest. Multidimensional advising across time The evidence advances prior work in several ways. First, we showed that doctoral advising consistently comprises psychosocial support, progress/feedback, networking/political capital, advisor capital/prestige, and publication/funding support, paralleling prior descriptions of advising functions and graduate socialization (Austin & McDaniels, 2006; Weidman et al., 2001 ). Notably, the item “advisor provides information about career paths” clustered with progress/feedback in earlier years, but moved to the category of networking by year-4. Such a change suggests that the functional meaning of the same practice may evolve: early career talk is embedded in degree-progress, program structures conversations, while it shifts outward to market navigation and professional communities later (Gardner, 2009 ; Weidman et al., 2001 ). While these patterns in our current study are specific to biology in research intensive contexts in the U.S., they move beyond a generic “mentoring matters” (Authors, 2024) claim by offering a time-sensitive measurement frame that can be refined and tested for invariance in other fields and institutional settings. Early advising and faculty career interest Second, we applied a block-wise approach to answer our two research questions: a) how do early and late advising relate to year-4 faculty career interest? and b) do associations of early and late advising with year-4 faculty career interest persist after controlling for starting plans? We found that early, structured progress/feedback practices and early scholarly production are the clearest predictors of later faculty interest, while later-stage advising adds little once early conditions are in place. In our context, students who secure early, structured feedback and publication experiences are not only “ahead” chronologically; they might as well better aligned with institutional expectations of being “on time” for an academic career (Bennett & Burke, 2018 ). From a temporal standpoint, the limited association between late advising and faculty interest may reflect the constraints of “compressed time,” where students receive more intensive career advising only after many key milestones have already passed. In such cases, even well-intentioned late support may leave little room to reorient toward an academic career. Additionally, the significance of early career interest also suggests that early experiences accumulate into expectations about what is possible. The positive association between early publication activity year-4 faculty career interest is not surprising, given that multiple studies support the idea that higher publication productivity predicts stronger alignment with, and pursuit of, faculty careers (Gibbs et al., 2014 ; Tregellas et al., 2018 ). This interpretation may help explain why early advising may matter more than later advising. Phase-based models of doctoral socialization (Weidman et al., 2001 ) hold that students move from anticipatory/adjustment (mastering local norms and requirements) to integration (participating in the research community) and finally professional identification (seeing oneself as a member of the field). In that framework, our two significant early predictors, progress/feedback routines and early scholarly production, are likely the practices that enable movement through the first two phases. Systematic check-ins, milestone mapping, and constructive feedback supply the clarity and scaffolding needed to internalize program standards and to take on scholarly tasks (e.g., writing scholarly manuscripts and publishing), which facilitate participation in the research community. By year-4, students who had that early structure are more likely to have accumulated experiences that consolidate an academic identity, which shows up as higher faculty career interest. Similarly, the Social Cognitive Career Theory (SCCT; Lent et al., 1994) specifies that mastery experiences are the strongest drivers of self-efficacy, which, together with outcome expectations, shapes interests and goals. Students’ early publication measure is a direct proxy for mastery experiences, and the process of drafting, revising, submitting, and getting accepted are clear signals of self-efficacy. Stronger efficacy beliefs in a given domain are expected to foster interest in the career path that requires and rewards those competencies. Accordingly, higher self-efficacy in conducting research should be relevant to faculty research careers, where scholarly production is central to role performance and advancement (Lindahl et al., 2021 ; Stoilescu & McDougall, 2010 ). On the other hand, early progress/feedback plays a complementary SCCT role by creating conditions in which mastery experiences can occur. Students not only receive structured encouragement and actionable critique but also get to the stage of having something to write/submit. As they produce early publication, students see that scholarly effort can yield tangible returns, making the academic role appear instrumentally rewarding. Together, early structured feedback and early scholarly production strengthen both self-efficacy and outcome expectations, which together are associated with stronger subsequent interest in research-intensive faculty careers. Lastly, our findings should be read as guidance for aligning advising practices with students’ goals, not as advocacy for faculty careers. Our outcome of interest—faculty career interest—does not imply a normative ranking of careers. Faculty intention is treated as an analytically convenient indicator of one major branch point because it is salient in doctoral culture (Gibbs & Griffin, 2013 ), highly advisor-sensitive (Sherman et al., 2021 ) and widely used in national reporting such as the Survey of Earned Doctorates (NCSES, 2025). Our goal is to illuminate how advising timing relates to the development of diverse career interests. Implications and future directions This finding extends prior advising research (Authors, 2024; Lindén et al., 2013 ) by pinpointing when and what advising matters the most. The results indicate that advising is most consequential when it is structured early and purposefully oriented later. Early in the program, advising that routinizes progress via clear milestones, scheduled check-ins with written action items, and explicit navigation of degree requirements, appears to create the conditions under which students engage in substantive scholarly work. When paired with scaffolded opportunities for publication, such as writing groups and research collaboration (Kwan et al., 2021 ; Author et al et al., 2025; McGrail et al., 2006 ), these practices convert effort into mastery experiences that are plausibly related to subsequent faculty career interest. By contrast, our models indicate that, at least for this dataset, late-stage advising showed little unique association with year-4 interest once early structures and early scholarly production were accounted for. We do not interpret this as evidence that late advising is unimportant. Rather, it suggests a potential practice-intent gap: late advising, in its current form, may be oriented toward administrative clearance or general encouragement rather than targeted conversations or experiences that could plausibly shift students’ career interests. On the other hand, prior work (Shermann et al., 2021) suggests that as doctoral training progresses, students’ interests increasingly diversify into a range of academic and nonacademic pathways. It is possible that late‑stage advising still matter, but its effects are likely distributed across diverse pathways instead of concentrated on strengthening faculty‑career interest. Future research could therefore examine how late-stage advising relates to students’ career interests in nonfaculty academic roles. To better support and prepare STEM students for diverse career trajectories, programs can respond by embedding late-stage professional development within existing structures and aligning delivery, content, and incentives with doctoral realities. For example, the Opportunities for Professional Training in Occupations for Scientists (OPTIONS) program developed by Johns Hopkins University is an explicit model of embedding multi‑phase career readiness into doctoral education (Neely et al., 2025 ). It offers late‑stage experiential learning such as internships and advising that expand students’ professional network before graduation. Importantly, the delivery of this type of practice should be feasible for busy graduate students and is encouraged to implemented through asynchronous preparation with repeated offerings across the term, or organized as default-in components connected to routine milestones. To assess whether these adjustments matter, programs can track simple indicators, such as the share of mid-program undecided students who, after late advising, report a concrete career plan. Future research can utilize mixed methods designs to capture how students and advisors enact these routines and to evaluate these interventions. Conclusion This study advances a developmental account of doctoral advising by showing the diverse effects of early and late advising in predicting faculty interest upon graduation. These findings shift the conversation away from a universal assessment of “good” advising, and suggest that advising should be understood as a time-sensitive developmental process whose functions may differ across stages of doctoral training. Declarations Ethics declaration We used de-identified secondary data from the Early Career Research (ECR) project (Feldon et al., 2023), which is funded by the National Science Foundation [#1431234, 1431290, 1760894] and is publicly available via the Open Science Framework (https://osf.io/hymus/). For the present study, an exempt determination for secondary data analysis was granted by the authors’ institutional review board, as the dataset contains no identifiable information. Funding This research received no external funding. References Austin, A. E. (2002). Preparing the next generation of faculty: Graduate school as socialization to the academic career. The Journal of Higher Education , 73 (1), 94-122. Austin, P. C., White, I. R., Lee, D. S., & van Buuren, S. (2021). Missing data in clinical research: a tutorial on multiple imputation. Canadian Journal of Cardiology, 37 (9), 1322-1331. Baker, V. L., & Pifer, M. J. (2011). The role of relationships in the transition from doctoral student to independent scholar. Studies in Continuing Education , 33 (1), 5-17. Bennett, A., & Burke, P. J. (2018). Re/conceptualising time and temporality: An exploration of time in higher education. Discourse: Studies in the Cultural Politics of Education , 39 (6), 913-925. Bodner, T. E. (2008). What improves with increased missing data imputations?. Structural Equation Modeling: A Multidisciplinary Journal, 15 (4), 651-675. Chang, C. N., Patterson, C. A., Vanderford, N. L., & Evans, T. M. (2021). Modeling individual development plans, mentoring support, and career preparedness relationships among doctor of philosophy (Ph. D.) trainees in the life sciences. F1000Research , 10 , 626. Charlesworth, T. E., & Banaji, M. R. (2019). Gender in science, technology, engineering, and mathematics: Issues, causes, solutions. Journal of Neuroscience , 39 (37), 7228-7243. Cornér, S., Löfström, E., & Pyhältö, K. (2017). The relationships between doctoral students’ perceptions of supervision and burnout. International Journal of Doctoral Studies, 12 , 91-106. De Welde, K., & Laursen, S. L. (2008). The “ideal type” advisor: How advisors help STEM graduate students find their ‘scientific feet’. The Open Education Journal , 1 (1), 49-61. Dericks, G., Thompson, E., Roberts, M., & Phua, F. (2019). Determinants of PhD student satisfaction: the roles of supervisor, department, and peer qualities. Assessment & Evaluation in Higher Education. Ganning, J. (2024). Doctoral education and the academic job market in planning. Journal of Planning Education and Research , 44 (3), 1063-1077. Gardner, S. K. (2009). The Development of Doctoral Students--Phases of Challenge and Support. ASHE Higher Education Report, 34 (6), 1-127. Gardner, S. K., & Mendoza, P. (Eds.). (2023). On becoming a scholar: Socialization and development in doctoral education. Taylor & Francis. German, K. T., Sweeny, K., & Robbins, M. L. (2019). Investigating the role of the faculty advisor in doctoral students’ career trajectories. Professional Development in Education , 45 (5), 762-773. Gibbs Jr, K. D., & Griffin, K. A. (2013). What do I want to be with my PhD? The roles of personal values and structural dynamics in shaping the career interests of recent biomedical science PhD graduates. CBE---Life Sciences Education , 12 (4), 711-723. Gibbs Jr, K. D., McGready, J., Bennett, J. C., & Griffin, K. (2014). Biomedical science Ph. D. career interest patterns by race/ethnicity and gender. PloS one , 9 (12), e114736. Ginther, D. K. (2021). Gender, race, and academic career outcomes: Does economics mirror other disciplines?. NBER Reporter , (3), 22-26. Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8 (3), 206-213. Hu, D., Park, H. J., Ruberton, P. M., Smyth, J. M., Cohen, G. L., Purdie-Greenaway, V., & Cook, J. E. (2026). Trust in advisor predicts Ph. D. students' academic motivation, well-being, and achievement: A prospective longitudinal study. PNAS nexus, 5 (1), pgaf411. Ibarra, H., Carter, N. M., & Silva, C. (2010). Why men still get more promotions than women. Harvard Business Review , 88 (9), 80-85. Jennrich, R. I., & Sampson, P. F. (1966). Rotation for simple loadings. Psychometrika, 31 (3), 313-323. Jeong, S., Blaney, J. M., & Feldon, D. F. (2019). Identifying faculty and peer interaction patterns of first-year biology doctoral students: A latent class analysis. CBE---Life Sciences Education , 18 (4), ar59. Kish, L. (1965). Survey sampling. Kwan, P. P., Sharp, S., Mason, S., & Saetermoe, C. L. (2021). Faculty writing groups: The impact of protected writing time and group support. International Journal of Educational Research Open , 2 , 100100. Lane, M., Dooley, K., Cavu, K., & Jaatinen, E. (2025). Aspiration versus outcome: the career intentions of PhD students in an Australian university. Studies in Graduate and Postdoctoral Education . Lindahl, J., Colliander, C., & Danell, R. (2021). The importance of collaboration and supervisor behaviour for gender differences in doctoral student performance and early career development. Studies in Higher Education , 46 (12), 2808-2831. Lindén, J., Ohlin, M., & Brodin, E. M. (2013). Mentorship, supervision and learning experience in PhD education. Studies in Higher Education , 38 (5), 639-662. Lopes, J., Ranieri, V., Lambert, T., Pugh, C., Barratt, H., Fulop, N. J., ... & Best, D. (2017). The clinical academic workforce of the future: a cross-sectional study of factors influencing career decision-making among clinical PhD students at two research-intensive UK universities. BMJ open , 7 (8), e016823. Maher, M. A., Wofford, A. M., Roksa, J., & Feldon, D. F. (2020). Finding a fit: Biological science doctoral students’ selection of a principal investigator and research laboratory. CBE—Life Sciences Education , 19 (3), ar31. Mansson, D. H., & Myers, S. A. (2012). Using mentoring enactment theory to explore the doctoral student–advisor mentoring relationship. Communication Education , 61 (4), 309-334. McGrail, M. R., Rickard, C. M., & Jones, R. (2006). Publish or perish: A systematic review of interventions to increase academic publication rates. Higher Education Research & Development , 25 (1), 19-35. National Academies of Sciences, Engineering, and Medicine. (2025). Reimagining STEMM Graduate Education and Postdoctoral Career Development: Proceedings of a Summit---in Brief. National Center for Science and Engineering Statistics (NCSES). 2025. Doctorate Recipients from U.S. Universities: 2024 Data Tables . NSF 25-349. Alexandria, VA: U.S. National Science Foundation. Available at https://ncses.nsf.gov/surveys/earned-doctorates/2024. National Institutes of Health. (2019). Strengthening the biomedical research workforce . Neely, C. J., Abras, C., Lauka, B., Oladeinde, M., & Eith, C. A. (2025). Bridging the gap: the OPTIONS program as a model for integrating career development into biomedical PhD training. In Frontiers in Education (Vol. 10, p. 1478553). Frontiers Media SA. Nersesian, P. V., Starbird, L. E., Wilson, D. M., Marea, C. X., Uveges, M. K., Choi, S. S. W., ... & Cajita, M. I. (2019). Mentoring in research-focused doctoral nursing programs and student perceptions of career readiness in the United States. Journal of Professional Nursing , 35 (5), 358-364. Noe, R. A. (1988). An investigation of the determinants of successful assigned mentoring relationships. Personnel Psychology, 41 (3), 457-479. Paglis, L. L., Green, S. G., & Bauer, T. N. (2006). Does adviser mentoring add value? A longitudinal study of mentoring and doctoral student outcomes. Research in Higher Education , 47 (4), 451-476. Pifer, M. J., & Baker, V. L. (2016). Stage-based challenges and strategies for support in doctoral education: A practical guide for students, faculty members, and program administrators. International Journal of Doctoral Studies , 11 , 15. Pinheiro, D. L., Melkers, J., & Newton, S. (2017). Take me where I want to go: Institutional prestige, advisor sponsorship, and academic career placement preferences. PloS one , 12 (5), e0176977. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology , 88 (5), 879. Portnoi, L. M., Chlopecki, A. L. A., & Peregrina-Kretz, D. (2015). Expanding the doctoral student socialization framework: The central role of student agency. The Journal of Faculty Development , 29 (3), 5-16. Roach, M., & Sauermann, H. (2017). The declining interest in an academic career. PloS one , 12 (9), e0184130. Sarrico, C. S. (2022). The expansion of doctoral education and the changing nature and purpose of the doctorate. Higher Education , 84 (6), 1299-1315. Sauermann, H., & Roach, M. (2012). Science PhD career preferences: levels, changes, and advisor encouragement. PloS one , 7 (5), e36307. Scalo, J., & Freauff, L. (2020). Evaluation of the National Institutes of Health (NIH) Broadening Experiences in Scientific Training (BEST) Program . Sedgwick, P. (2012). What is recall bias?. Bmj , 344 . Sherman, D. K., Ortosky, L., Leong, S., Kello, C., & Hegarty, M. (2021). The changing landscape of doctoral education in science, technology, engineering, and mathematics: PhD students, faculty advisors, and preferences for varied career options. Frontiers in Psychology , 12 , 711615. Skakni, I., Kereselidze, N., Parmentier, M., Delobbe, N., & Inouye, K. (2025). PhD graduates pursuing careers beyond academia: a scoping review. Higher Education Research & Development , 1-20. Skinnider, M. A., Twa, D. D., Squair, J. W., Rosenblum, N. D., Lukac, C. D., & Canadian MD/PhD Program Investigation Group. (2018). Predictors of sustained research involvement among MD/PhD programme graduates. Medical Education , 52 (5), 536-545. St. Clair, R., Hutto, T., MacBeth, C., Newstetter, W., McCarty, N. A., & Melkers, J. (2017). The “new normal”: Adapting doctoral trainee career preparation for broad career paths in science. PloS one , 12 (5), e0177035. Stoilescu, D., & McDougall, D. (2010). Starting to publish academic research as a doctoral student. International Journal of Doctoral Studies , 5 , 79-92. Ten Berge, J. M., Krijnen, W. P., Wansbeek, T., & Shapiro, A. (1999). Some new results on correlation-preserving factor scores prediction methods. Linear Algebra and Its Applications, 289 (1-3), 311-318. Tregellas, J. R., Smucny, J., Rojas, D. C., & Legget, K. T. (2018). Predicting academic career outcomes by predoctoral publication record. PeerJ, 6 , e5707. Wapman, K. H., Zhang, S., Clauset, A., & Larremore, D. B. (2022). Quantifying hierarchy and dynamics in US faculty hiring and retention. Nature , 610 (7930), 120-127. Weidman, J. C., Twale, D. J., & Stein, E. L. (2001). Socialization of Graduate and Professional Students in Higher Education: A Perilous Passage? ASHE-ERIC Higher Education Report, Volume 28, Number 3. Jossey-Bass Higher and Adult Education Series . Jossey-Bass, Publishers, Inc., 350 Sansome Street, San Francisco, CA 94104-1342. West, M., McCain, J., & Roksa, J. (2024). After the PhD: the role of advisors and social connections in the job search process. Studies in Graduate and Postdoctoral Education , 15 (3), 380-394. Wood, C. V., Jones, R. F., Remich, R. G., Caliendo, A. E., Langford, N. C., Keller, J. L., ... & McGee, R. (2020). The National Longitudinal Study of Young Life Scientists: Career differentiation among a diverse group of biomedical PhD students. Plos one , 15 (6), e0234259. Yang, Y., & Cai, J. (2022). Profiles of PhD students’ satisfaction and their relationships with demographic characteristics and academic career enthusiasm. Frontiers in Psychology, 13 , 968541. Zhao, C. M., Golde, C. M., & McCormick, A. C. (2007). More than a signature: How advisor choice and advisor behaviour affect doctoral student satisfaction. Journal of Further and Higher education, 31 (3), 263-281. Additional Declarations No competing interests reported. Supplementary Files WhenAdvisingMattersAppendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9051283","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611642811,"identity":"f605c1e6-68fd-401c-8715-ddbaa7c48c49","order_by":0,"name":"Lechen Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPgYGNiBlwcPPwPjwAAMDM2EtbBAtEjySDcwGpGlhMDhAtBb25mcPPu6QkDG+kcxwgKHCOrGBoBaeY+aGM89I8JiBtZxJJ0KLRA6bNG8bSEv+gQOMbYeJ0CL/hk36L1CL8QygLYz/iNEiwcMmzQjUYiAB0tJAjBaeNDPJXqAWiTOPGQ4kHEs3JqiFn/3wM4mfbTb2/O3JjA8+1FjLEtSCChJIUz4KRsEoGAWjABcAAH43NmfUjUuxAAAAAElFTkSuQmCC","orcid":"","institution":"University of Florida","correspondingAuthor":true,"prefix":"","firstName":"Lechen","middleName":"","lastName":"Li","suffix":""},{"id":611642814,"identity":"605a81e0-74a1-4ebb-9c8d-5bc3309dbb9a","order_by":1,"name":"Jue Wu","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Jue","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-03-06 13:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9051283/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9051283/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566777,"identity":"5957b506-ef97-4284-869f-1a1bc8eb6207","added_by":"auto","created_at":"2026-03-27 12:57:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1468333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9051283/v1/630776be-8d8e-49f7-8f16-f0ae3418a5c3.pdf"},{"id":105452991,"identity":"7fb13dd4-fbe2-412e-bbcb-d216efb87215","added_by":"auto","created_at":"2026-03-26 08:37:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20729,"visible":true,"origin":"","legend":"","description":"","filename":"WhenAdvisingMattersAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9051283/v1/102e59deb5776e8cdddd56db.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Advising Matters: Early Versus Late Faculty Advising and STEM Doctoral Students' Faculty Career Interests","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEach year, U.S. doctoral recipients disperse across a wide range of sectors, with the majority moving outside academic employment. In 2024, among Science and Engineering (S\u0026amp;E) doctorate recipients who had definite non-postdoc job commitments in the United States, 52.4% committed to industry/business while 30.1% committed to academia (National Center for Science and Engineering Statistics (NCSES), \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In several Science, Technology, Engineering, and Mathematics (STEM) fields, the academic share was even lower (e.g., Engineering (13.9%), Physical Science (17.9%)), indicating how commonly doctoral training culminates in non-faculty roles.\u003c/p\u003e \u003cp\u003eBeyond this broad sectoral shift, faculty hiring remains sharply bottlenecked and prestige-stratified. Drawing on 2011\u0026ndash;2020 academic employment from all U.S. PhD-granting universities, Wapman et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) find that a small fraction of institutions supply a disproportionate share of tenure-track faculty and hiring patterns reproduce status hierarchies across the system. Additionally, a recent report (National Academies of Sciences, Engineering, and Medicine, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) discusses the funding cuts and sociopolitical changes: grant cancellations, substantial National Institute of Health (NIH) and National Science Foundation (NSF) budget cuts, and tighter immigration policies constrict the pipeline for early-career researchers.\u003c/p\u003e \u003cp\u003eDespite this diversification and challenges, doctoral culture often continues to signal faculty careers as the default metric of success. In shaping doctoral graduates\u0026rsquo; career trajectories, advising matters in particular for the faculty pathway: not only do students gain stronger interest in research and academic self‑efficacy that help developing more optimism in academic career (Hu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), many also perceive their advisors as more comfortable discussing and encouraging research-intensive academic paths (Sherman et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, program-level reforms with structured career exploration and advising are shown to improve career knowledge and decision confidence without increasing time-to-degree (Scalo \u0026amp; Freauff, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), suggesting the importance of intentional advising in broaden preparation. The empirical studies examining the association between advising and career trajectories have one persistent limitation: Most studies (e.g., Lopes et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sauermann \u0026amp; Roach, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) are \u0026ldquo;cross-sectional\u0026rdquo;, leaving unanswered when and how advising exerts the most leverage. This contrasts with stage-oriented socialization theory (Weidman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and others that view advising as dynamic across the doctoral journey. Beyond methodological limitations, higher education research rarely considers how the \u003cem\u003etiming\u003c/em\u003e of support is organized by institutional expectations. Bennett and Burke (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) argue that universities build informal timelines for when students are expected to develop, make decisions, and produce work, and that these expectations are shaped by unequal access to time, funding, and care resources. In doctoral education, such timelines are reflected in assumptions about when students should be developing as researchers, publishing, and preparing for the job market.\u003c/p\u003e \u003cp\u003eThis study aims to address that evidence-theory gap by conceptualizing advising timing and analyzing the relationship between multi-wave advising measures and students\u0026rsquo; career interest. Data comes from a national, longitudinal sample of biology doctoral students across 53 research-intensive universities in U.S. Biology is a STEM discipline that not only shows relatively stronger faculty career interest among doctoral students than many other STEM fields (NCSES, 2025), but is also typically organized around laboratory apprenticeship, where career development is closely tied to advising and disciplinary socialization (Maher et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We used factor analysis to interpret domains of advising practices and conducted ANOVA with Tukey test to compare students\u0026rsquo; trajectories of faculty career interest. For inferential statistics, we estimated block-wise regression models on students\u0026rsquo; faculty career interest in year-4.\u003c/p\u003e \u003cp\u003eAs doctoral education grows (Sarrico, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the academic labor market remains unsettled (Ganning, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), timing in advising takes on practical consequences. By tracing how advising unfolds across time rather than at a single snapshot, this study speaks to students, faculty, programs, and the wider research in doctoral education (Wood et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Importantly, the aim of this study is not to steer students toward any single pathway but to align advising with diverse goals. Against the backdrop of a volatile academic market, such as constrained research budgets, uneven hiring, and intensified competition, this study offers actionable knowledge and implications for students, advisors, and programs seeking to make doctoral training responsive, equitable, and future-oriented.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eDoctoral education is simultaneously a training pipeline for conducting research in both academic and professional field (Sarrico, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and a socialization process that shape students\u0026rsquo; professional identities (Gardner \u0026amp; Mendoze, 2023). Through research training and advisor-mentoring that shape skills, identities, and access to opportunities, they pursue roles across academia, industry, government, nonprofit sector, and students\u0026rsquo; preferences may shift during training as they gain information, skills, and networks (Gibbs Jr et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Roach \u0026amp; Sauermann, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStill, both scholarship and policy tracking typically frame early outcomes through the faculty vs. non-faculty lens: national monitoring (e.g., the NSF Survey of Earned Doctorates) reports sectoral intentions and commitments in academic versus non-academic categories. A recent scoping review by Skakni et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) likewise map the literature around career choices/intentions and employment outcomes, and they highlight how \u0026ldquo;academic vs. beyond academia\u0026rdquo; remains the dominant way researchers describe doctoral careers.\u003c/p\u003e \u003cp\u003e​\u003cb\u003eAdvising and doctoral career development\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdvisors play an instrumental role in shaping the professional and career trajectories of doctoral students by providing mentorship (Manson \u0026amp; Myers, 2012), socialization into academic and professional communities (Portnoi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and guidance through the complexities of research and career decision-making processes (German et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In particular, they significantly influence students\u0026rsquo; persistence toward degree completion (Paglis et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), career interest and readiness (Chang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nersesian et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), thereby contribute broadly to the long-term work force development (De Welde \u0026amp; Laursen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNot all advising yields the same result on career outcome, and structured career advising remains insufficient within many doctoral programs (Austin, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For instance, West et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) find that the majority students do not rely on their advisor during job search, regardless of the \u0026ldquo;type\u0026rdquo; of advisors. A practical distinction in \u0026ldquo;advisor type\u0026rdquo; is the difference between mentorship and sponsorships: Compared to a mentor, a sponsor not only \u0026ldquo;giving feedback and advice and uses his or her influence with senior executives to advocate for the mentee\u0026rdquo; (Ibarra et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, p. 82).\u003c/p\u003e \u003cp\u003eIn a quantitative study, Pinheiro et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examine patterns of doctoral publication, advisor advocacy, and subsequent scholarly productivity through the \u0026ldquo;sponsorships\u0026rdquo; lens. They find that students whose advisors actively promote their work through co-authorship and professional introductions are more successful in securing research positions. In their study, male students are more likely to benefit from such sponsorship behaviors, suggesting a potential gendered disparity in how advisors\u0026rsquo; influence translated into visible academic capital. Complementing this, West et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) conduct a qualitative exploration of sponsorship in biological sciences doctoral programs in the US, examining how faculty advisors and other individuals shape students\u0026rsquo; experiences during the job search process. Drawing on interviews with 47 doctoral students in biological sciences, they identify \"sponsorship advisors\" as those who \u0026ldquo;go beyond providing general support to leverage their personal networks to assist in the transition to full-time employment after graduation\u0026rdquo; (p. 381). For example, when a doctoral student (Jane) expressed interest in pursuing an industry career, her advisor provided sponsorship by acknowledging his own limitations and lack of expertise in that domain and introduced a secondary mentor who held a full-time position at a pharmaceutical company to bridge this gap. These studies suggest that effective advising on students\u0026rsquo; career outcomes could be practice-specific, and uneven access to those practices may help explain disparities in outcomes (Charlesworth \u0026amp; Banaji, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to advising, prior studies also report several variables that predict doctoral students\u0026rsquo; career development. For example, faculty career interests and outcomes are influenced by students\u0026rsquo; demographics and backgrounds (e.g., Ginther, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Roach \u0026amp; Sauermann, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tregellas et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as these characteristics are often associated with differential access to resources, networks, and feelings of belonging in academia. Socialization with faculty is consistently viewed as key predictors of doctoral outcomes (Gardner, 2010; Flores-Scott and Nerad, 2012). Often broader than advising, faculty-socialization encompasses interactions that support academic development, professional identity formation, and career preparation (Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). On the other hand, while peer-socialization receives less attention in existing literature (Jeong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a few studies suggest that doctoral graduate interact with peers more frequently than with faculty (Weidman \u0026amp; Stein, 2003), and that this peer relationship can contribute to students\u0026rsquo; experiences and outcomes (e.g., Dericks et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lastly, satisfaction with institutional service and resources, including perceived quality of career development, skills training, and overall doctoral experience, is positively related to confidence in achieving desired career outcomes (Lane et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Yang and Cai\u0026rsquo;s (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) study on doctoral student satisfaction also show that students who report stronger satisfaction with supervision, program structure, and research conditions tend to report higher interest in academic research careers.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTiming and developmental nature of doctoral advising\u003c/h2\u003e \u003cp\u003eTheoretical literature often conceptualizes doctoral development as stage-based and interactive. In the socialization framework, for instance, Weidman et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) propose a dynamic, non-linear model in which doctoral socialization merges students\u0026rsquo; prior inputs with faculty, peer, and community influences across personal, professional, and academic domains, emphasizing connectedness and networking that spans graduate school and postgraduation. Similarly, in the conceptual work by Baker and Pifer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), they outline three stages in doctoral education: (1) coursework/dependence, (2) transition toward independence (e.g., establishing writing routines, engaging in ongoing professional development), and (3) transition to scholar/long-term planning (e.g., developing a research agenda, receiving candid guidance about the academic profession and promotion/tenure).\u003c/p\u003e \u003cp\u003eBecause advising is one of the primary relational mechanisms through which programs support students\u0026rsquo; progress and professional identity development, its content and consequences are likely to vary depending on when it occurs in the training trajectory. Pifer and Baker (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) extend this idea by framing phases in doctoral training as knowledge consumption, knowledge creation, and knowledge enactment, arguing that each stage carries distinct advising functions: early navigational advising and legitimation of career exploration; mid-stage skill building via experiential opportunities and multiple mentoring sources; and late-stage sponsorship through introductions, recommendations, and advising for diverse careers, while also noting that challenges cluster both within stages and at the transitions between them. Advising timing, therefore, has become a theoretical expectation about when particular advising functions should matter most.\u003c/p\u003e \u003cp\u003eDespite these stage-based expectations, the advising-careers literature remains predominantly cross-sectional, and they often examine career encouragement/support to contemporaneous intentions without taking time into consideration (e.g., Sauermann \u0026amp; Roach, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; St. Clair et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sherman et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Corn\u0026eacute;r et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lopes et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As an example, the study by Sauermann and Roach (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) is based on a single 2010 survey of 4,109 doctoral students at 39 U.S. universities, and the \u0026ldquo;changes\u0026rdquo; in career preference were inferred by comparing cohorts and retrospective reports rather than following the same individuals over time. The authors explicitly note this limitation and call for \u0026ldquo;multiple real-time measurements\u0026rdquo; in future work.\u003c/p\u003e \u003cp\u003eTaking together, existing research establishes advisors as essential to doctoral career development, identifies practices that improve advising quality, and shows that students\u0026rsquo; intentions evolve across training. What remains underdeveloped is a time-sensitive account of when advising matters for which mechanisms and outcomes. To address this gap, we model advising as a stage-contingent, dynamic set of influences, particularly within the life sciences where academic labor markets are tight and career diversification is common (Gibbs et al., 2013; Wood et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We ask two research questions: (a) how do year-1 advising and year-4 advising relate to year-4 faculty career interest? and (b) do associations of early and late advising with year-4 faculty career interest persist after controlling for starting plans (year-1 interest)?\u003c/p\u003e \u003c/div\u003e"},{"header":"Method","content":"\u003cp\u003eWe analyzed de-identified secondary data from the Early Career Research (ECR) project (Feldon et al., 2023), a publicly archived dataset on the Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/hymus/\u003c/span\u003e\u003cspan address=\"https://osf.io/hymus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). ECR is a mixed-methods longitudinal panel that tracks the developmental trajectories of 336 biology doctoral students who began their doctoral studies in Fall 2014 across 53 research-intensive U.S. universities. Of the 336 participants, 132 (~\u0026thinsp;40%) are men and 200 (~\u0026thinsp;60%) are women, with four didn\u0026rsquo;t respond. 66 (19.6%) participants are international students. Racial and ethnic identification was predominantly White (n\u0026thinsp;=\u0026thinsp;200; ~60%), followed by Asian (n\u0026thinsp;=\u0026thinsp;71; ~21%), Black or African American (n\u0026thinsp;=\u0026thinsp;21; ~6%)), and Hispanic or Latino (n\u0026thinsp;=\u0026thinsp;26; ~8%), and multiracial or other (n\u0026thinsp;=\u0026thinsp;18; ~5%). To reduce sparseness in models, we collapsed race to White, Asian, and Minority (combining Black, Hispanic/Latine, Native American, Pacific Islander, Other, and Multiple) in the current study, an approach previously applied to this dataset by Jeong et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEach year, students complete an end-of-year survey capturing demographics, degree-progress indicators, perceptions of program quality and climate, socialization, research and publication activity, graduate advising, and related experiences. The dataset has supported prior quantitative and qualitative studies of doctoral development (e.g., Feldon, 2016; Roksa et al., 2018; Jeong et al., 2020; Zhang et al., 2022). In the current study, we drew explicitly from the first to fourth waves of the annual survey: this window provides sufficient time to observe within-person change for longitudinal analysis, while preserving a relative complete panel (later waves contain substantially more missingness due to attrition).\u003c/p\u003e \u003cp\u003eWe used multiple imputations by chained equations with a rich predictor set that included prior outcomes. Missing data was handled using multiple imputations by chained equations (MICE). The overall missing rate was 15.97% across the unimputed dataset, and the missing rate of key dependent variable (year-4 faculty interest) is 25.36%. In their methodological guidance on multiple imputation, Austin et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) proposed that \u0026ldquo;multiple imputation is blind to which variables are outcomes and which variables are predictors\u0026rdquo; (p. 1326). Therefore, we imputed all variables from the analysis model.\u003c/p\u003e \u003cp\u003eVariables were specified according to measurement level (e.g., ordinal items coded 1\u0026ndash;3, binary indicators coded 0/1, and year of birth treated as continuous). Little\u0026rsquo;s MCAR test for the CI outcome block was nonsignificant, χ\u0026sup2;(425)\u0026thinsp;=\u0026thinsp;422, p = .533, providing no evidence against MCAR for these outcomes. We generated 40 imputed datasets (m\u0026thinsp;=\u0026thinsp;40) using 10 iterations (maxit\u0026thinsp;=\u0026thinsp;10) with a fixed random seed to improve precision, in line with recommendations to use a relatively large number of imputations when the fraction of missing information is moderate (Bodner, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Graham et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Imputation models were specified by variable type and used an automatically constructed predictor matrix (quickpred; minpuc = .60), excluding participant identifiers and removing constant terms. Sensitivity analyses comparing pooled MI results with complete-case estimates yielded substantively similar conclusions.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eDependent variables\u003c/em\u003e. Because the outcome is explicitly about \u0026ldquo;faculty-track\u0026rdquo; interest, the dependent variable is students\u0026rsquo; year-4 faculty career interest (currently, are you interested in or want to pursue each of these career options?- To become a professor in a college or university), measured on a 3-point ordered scale (1\u0026thinsp;=\u0026thinsp;Not at all, 2\u0026thinsp;=\u0026thinsp;Possibly, 3\u0026thinsp;=\u0026thinsp;Definitely). We also captured their faculty career interests at the beginning of their programs to construct individual interest trajectories across time.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIndependent variables.\u003c/em\u003e The central independent variables are advising practices. Students complete a set of advising items (e.g., My primary advisor treats me with respect; My primary advisor provides information about career paths open to me) each year, rated on a three-point Likert scale (1\u0026thinsp;=\u0026thinsp;Disagree, 2\u0026thinsp;=\u0026thinsp;Neutral, 3\u0026thinsp;=\u0026thinsp;Agree).\u003c/p\u003e \u003cp\u003eWe conducted exploratory factor analyses for the year-1 and year-4 advising item blocks using the completed imputed dataset. Because the indicators were ordinal, we estimated the factor model using a polychoric correlation matrix. Parallel analysis indicated five factors in each wave, and we applied oblimin rotation (Jennrich \u0026amp; Sampson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) because the advising dimensions were expected to be correlated (Noe, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Factor scores were computed using the Ten Berge method (Ten Berge et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) implemented in the \u0026ldquo;psych\u0026rdquo; package in R, which preserves the correlations among obliquely rotated factors and were centered at zero by construction (so the mean of each factor score are approximately 0). We then treated those factor scores as continuous measures of the underlying advising dimensions and used them as predictors in subsequent regression models.\u003c/p\u003e \u003cp\u003e \u003cem\u003eControl variables\u003c/em\u003e. We first controlled baseline demographics, including age, gender (0\u0026thinsp;=\u0026thinsp;Male, 1\u0026thinsp;=\u0026thinsp;Female), race, and citizenship (0\u0026thinsp;=\u0026thinsp;Citizen, 1\u0026thinsp;=\u0026thinsp;International). To reduce sparseness in models, we collapsed race to White, Asian, and Minority (combining Black, Hispanic/Latine, Native American, Pacific Islander, Other, and Multiple), an approach previously applied to this dataset by Jeong et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We also adjusted for faculty/peer socialization and scholarly productivity in \u0026ldquo;early\u0026rdquo; and \u0026ldquo;late forms. Each year, students reported whether they engage with faculty and peers in four interaction types: social matters, field-related topics, intellectual interests and personal matters, using binary responses (0\u0026thinsp;=\u0026thinsp;no, 1\u0026thinsp;=\u0026thinsp;yes). We summed these four items within each year to create a socialization count, then averaged the year-specific counts across years 1\u0026ndash;2 and years 3\u0026ndash;4 to form early/late faculty socialization and early/late peer socialization, each ranging from 0 to 4. Similarly, conference presentations and publications were coded as two-year sums (year 1\u0026ndash;2, year 3\u0026ndash;4), producing early/late conference and early/late publication, all ranging from 0 to 2.\u003c/p\u003e \u003cp\u003eThis separation allowed us test whether early and late experiences differentially align with later faculty interest. Although we lacked university identifiers to cluster by institution, this sample is predominantly research-intensive, which likely reduces between-institution heterogeneity relative to mixed-sector samples. We also included composites of institutional resources (early/late satisfaction with services and facilities), using similar approaches, as proxies for campus context and conducted a design-effect sensitivity (Kish, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) that inflated standard errors under plausible within-university similarity. Results were substantively unchanged. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics for the dependent and independent variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of ECR doctoral respondents (n\u0026thinsp;=\u0026thinsp;330)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean / Percentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaculty career interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-1 faculty interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-4 faculty interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvising factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-1 psychosocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-1 network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-1 reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-1 degree/progress feedback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-1 publication/ funding support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-4 psychosocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-4 reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-4 network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-4 publication, degree progress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear-4 early grad school transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (reference group: male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (reference group: White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizenship (reference group: citizen)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctoral experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly socialization with faculty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate socialization with faculty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly socialization with peers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate socialization with peers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly satisfaction with institution\u0026rsquo;s service and facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate satisfaction with institution\u0026rsquo;s service and facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly conference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate conference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly publication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate publication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eInferential statistics: Block-wise ordinal regression\u003c/h3\u003e\n\u003cp\u003eTo examine when and which advising dimensions predict later faculty interest while adjusting for confounds, we estimated cumulative-logit ordinal models with proportional odds and entered predictors in blocks. The first block included all control variables, including baseline demographics, early/late measures of socialization with faculty and peers, scholarly production, and satisfaction with institutional service and facilities. We then added the early advising factors (year-1) to assess whether early advising has durable associations with year-4 interest net of those controls. Next, we introduced the late advising factors (year-4) to test whether end-stage advising explains additional variance once early advising is held constant. Finally, we included baseline year-1 faculty interest to evaluate whether any advising effects persist beyond students\u0026rsquo; initial intentions.\u003c/p\u003e \u003cp\u003eWe examined the proportional-odds assumption using threshold-specific coefficients and heterogeneous residual scale and found no evidence against the parallel-lines assumption, suggesting the ordinal logit specification is appropriate. Additionally, we assessed likelihood-ratio tests to identify whether each added block of predictors provides meaningful extra explanatory power to the previous model.\u003c/p\u003e\n\u003ch3\u003eLimitation\u003c/h3\u003e\n\u003cp\u003eFirst, the study draws on a single discipline, biology, and a relatively small cohort (N\u0026thinsp;=\u0026thinsp;336) concentrated in research-intensive universities, meaning the estimates may not generalize to other fields or institutional sectors. Second, advising measures and outcomes are self-reports collected at year\u0026rsquo;s end. Common-method variance (Podsakoff et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and recall bias (Sedgwick, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) are possible, and perceptions of advising may be intertwined with broader program climate. Third, our findings identify correlational relationships rather than causal effects. Future research may use different data with institution identifiers to estimate cluster-robust or quasi-experimental approaches.\u003c/p\u003e \u003cp\u003eNevertheless, this study advances existing literature by introducing an intuitive, developmentally grounded timing perspective that separates early and late advising and traces their associations to faculty career interest over time. Because few prior studies have leveraged longitudinal designs to interrogate when advising matters, these results provide a novel framework and generate testable hypotheses for subsequent research using larger, more diverse samples and more rigorous identification strategies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFindings\u003c/h2\u003e \u003cp\u003eFirst, we report the year-specific exploratory factor analyses that recover core dimensions of advising (with exemplary items in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and full items in Appendix I). Then, we present regression models of year-4 faculty interest, adding predictors in blocks to assess incremental explanatory power.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor analysis results (exemplary items only; see full result in Appendix I)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePsycho-social support\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYear-4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy primary advisor treats me with respect; (0.68)\u003c/p\u003e \u003cp\u003eMy primary advisor cares about me as a person (0.79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePsycho-social support\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy primary advisor treats me with respect; (0.57)\u003c/p\u003e \u003cp\u003eMy primary advisor cares about me as a person. (0.96)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNetwork\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy primary advisor helps me get to know other faculty members at the University; (0.65)\u003c/p\u003e \u003cp\u003eMy primary advisor helps me get to know other faculty or professionals in my field. (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNetwork\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy primary advisor helps me get to know other faculty or professionals in my field; (0.75)\u003c/p\u003e \u003cp\u003eMy primary advisor provides information about career paths open to me. (0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCapital/prestige/reputation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy primary advisor was recommended to me by other people; (0.45)\u003c/p\u003e \u003cp\u003eMy primary advisor is well known in my discipline or field of study. (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCapital/prestige/reputation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy primary advisor was recommended to me by other people; (0.32)\u003c/p\u003e \u003cp\u003eMy primary advisor has a reputation for being a good advisor to graduate students. (0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDegree/progress feedback\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy primary advisor gives me constructive feedback on my progress toward degree completion; (0.50)\u003c/p\u003e \u003cp\u003eMy primary advisor provides information about career paths open to me. (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDegree/progress feedback, publication and funding support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy primary advisor gives me constructive feedback on my progress toward degree completion; (0.55)\u003c/p\u003e \u003cp\u003eMy primary advisor initiates discussions about the progress I am making toward my degree; (0.55)\u003c/p\u003e \u003cp\u003eMy primary advisor encourages me to publish; (0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResearch/funding support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy primary advisor encourages me to publish; (0.55)\u003c/p\u003e \u003cp\u003eMy primary advisor helps me secure funding for my graduate studies. (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGraduate school transition in year-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhen I first came to my university, my primary advisor helped me with the transition to graduate school. (0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes. Values in parentheses are standardized factor loadings. Items are assigned to factors based on their largest absolute loading\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFactor analysis: stable advising domains over time with modest developmental shifts\u003c/h3\u003e\n\u003cp\u003eExploratory factor analyses were conducted separately for year-1 and year-4 advising items. A five-factor solution was retained in each wave, and factors were labeled based on the highest-loading items (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For year-1 advising items, five conceptually stable domains emerged (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Psychosocial Support (e.g., advisor cares, respects, is available to talk when needed), Networking (introductions to faculty/professionals; navigating departmental politics), Degree Progress / Feedback (initiates progress conversations; constructive feedback; degree requirements), Advisor Capital / Prestige (advisor reputation, funding ability, timely completion), and Publication / Funding Support (encouragement to publish; funding help). At year-4, the EFA produced a similar set of advising domains, but with one modest reconfiguration in ways that are substantively consistent with developmental changes in doctoral training: the item \u0026ldquo;When I first came to my university, my primary advisor helped me with the transition to graduate school\u0026rdquo; formed a standalone factor rather than loading with other year-4 advising items. This is unsurprising, given the item is retrospective that explicitly asks respondents to evaluate an advisor behavior tied to program entry rather than the current advising relationship.\u003c/p\u003e \u003cp\u003eNotably, the item \u0026ldquo;my advisor provides information about career paths open to me\u0026rdquo; loaded with degree-progress in year-1 but with networking in year-4. This pattern aligns with developmental models of doctoral socialization in which early advising centers on adjustment to program structures and degree progress, while later advising emphasizes professional integration and external networks. In particular, the socialization theory (Weidman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Gardner\u0026rsquo;s (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) phase-based framework both anticipate a shift from progress/feedback to outward-facing professional socialization across the doctorate. Studies of advisor influence on evolving career intentions similarly indicate that career guidance becomes more tied to networking and labor-market navigation (e.g., Gibbs \u0026amp; Griffin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sauermann \u0026amp; Roach, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; St. Clair et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the item capturing advisors\u0026rsquo; encouragement of publication exhibited a shift in its factor category from year-1 to year-4. We interpret this movement as substantively consistent with the developmental organization of doctoral training. Early in the program, \u0026ldquo;encourages me to publish\u0026rdquo; may function as an initial push toward research productivity. By year-4, however, publication may become closely tied to concrete progress toward finishing the doctoral study and preparing for the job market, instead of simply \u0026ldquo;encouragement\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eInferential: advising timing and faculty career interest\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the summary of our block-wise ordinal logistic regression models. Recalling our modeling strategy, we estimated cumulative-logit models using block-wise incremental adjustment. In Model 1, the first block entered demographics and broad, non-advising program composites to account for experiences plausibly correlated with both advising and career interest. Model 2 included the second block, early (year-1) advising domains, to test whether early practices explain variance above and beyond those program factors. Model 3 added the third block, late (year-4) advising domains, to assess any incremental contribution of end-stage advising once early advising and program experiences are held constant. Model 4 included baseline year-1 faculty interest so that any remaining advising associations reflect changes or persistence beyond students\u0026rsquo; initial plans. This sequence lets us isolate whether advising\u0026mdash;especially early advising\u0026mdash;adds explanatory power over and above the broader doctoral context.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of block-wise regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly Advising\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1 psychosocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1 network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1 reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1 degree/progress feedback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.532***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.486**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.488**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1 publication/ funding support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLate Advising\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 psychosocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 publication, degree progress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 early grad school transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline interest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY1 faculty interest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.819***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (reference group: male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (reference group: White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizenship (reference group: citizen)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDoctoral experiences\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly socialization with faculty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate socialization with faculty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.455*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.459*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.380*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly socialization with peers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate socialization with peers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly satisfaction with institution\u0026rsquo;s service and facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate satisfaction with institution\u0026rsquo;s service and facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly conference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate conference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly publication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.455*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.544**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.583**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.535*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate publication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: Significance: *** p\u0026lt;.001, ** p\u0026lt;.01, * p\u0026lt;.05; Y1\u0026thinsp;=\u0026thinsp;year-1, Y4\u0026thinsp;=\u0026thinsp;year-4.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRQ1: how do early/late advising relates to students\u0026rsquo; year-4 faculty career interests\u003c/h2\u003e \u003cp\u003eModel 1 indicated that, holding other variables constant, late socialization with faculty (OR\u0026thinsp;=\u0026thinsp;1.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was positively associated with Year-4 faculty career interest, meaning that a one-unit increase in late faculty socialization was associated with 46% higher odds of reporting higher year-4 faculty-interest category. This pattern is consistent with prior studies suggesting that advisors are viewed as more comfortable discussing research positions and as preferring research roles for their advisees (Sherman et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Early publication activity (OR\u0026thinsp;=\u0026thinsp;1.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was also positively associated with increasing year-4 faculty interest. suggesting a 46% increase in the odds of being in a higher interest category.\u003c/p\u003e \u003cp\u003eAdding early advising (year-1) domains in Model 2 significantly improved model fit (Δ\u0026ndash;2LL\u0026thinsp;=\u0026thinsp;12.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In this model, year-1 degree progress/feedback advising emerged as a unique positive predictor (OR\u0026thinsp;=\u0026thinsp;1.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning that a one-unit increase in this scale was associated with 1.53 times the odds of being in a higher category of year-4 faculty career interest. By contrast, the effect of late socialization with faculty (OR\u0026thinsp;=\u0026thinsp;1.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and early publication (OR\u0026thinsp;=\u0026thinsp;1.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) remained robust.\u003c/p\u003e \u003cp\u003eIn Model 3, adding the Year-4 advising factors did not significantly improved fit over Model 2 (Δ\u0026ndash;2LL\u0026thinsp;=\u0026thinsp;10.5, p \u0026asymp; .06). Additionally, no late advising factor showed a significant association once early advising and other experiences were controlled. In comparison, late socialization with faculty (OR\u0026thinsp;=\u0026thinsp;1.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), early publication (OR\u0026thinsp;=\u0026thinsp;1.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and year-1 degree progress/feedback advising (OR\u0026thinsp;=\u0026thinsp;1.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) continued to be positively associated, and the effect sizes remained similar.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRQ2: Year-4 faculty career interest change: controlling for starting plans\u003c/h2\u003e \u003cp\u003eModel 4 added baseline year-1 faculty career interest, which substantially improved fit (Δ\u0026ndash;2LL\u0026thinsp;=\u0026thinsp;34.5, p \u0026lt; .001). It was positively correlated (OR\u0026thinsp;=\u0026thinsp;2.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with year-4 faculty career interest. This means that a one-unit increase in baseline year-1 faculty career interest is associated with 2.82 times higher odds of reporting a higher level of Year-4 faculty career interest. In addition, even conditioning on initial intentions, early publication (OR\u0026thinsp;=\u0026thinsp;1.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and early progress/feedback advising (OR\u0026thinsp;=\u0026thinsp;1.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) remained significant, which were equivalent to around 54% and 48% higher odds of being in a higher year-4 interest category per one-unit increase. This pattern indicates that these two early experiences were associated with gains or persistence in faculty interest over time, not merely cross-sectional differences among students who already intended an academic career. However, late socialization with faculty became insignificant, and none of the late advising domains were significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSummary of findings\u003c/h2\u003e \u003cp\u003eAcross the models, the results point to a clear timing pattern: early experiences appear to matter more than late experiences in predicting year-4 faculty career interest, at least in this dataset. In particular, early degree progress/feedback advising and early publication remained positive and statistically significant predictors even after adjusting for a broad set of covariates and baseline year-1 faculty career interest. By contrast, adding year-4 (late) advising factors did not yield meaningful incremental explanatory power and none of the late advising domains showed an independent association once early advising and prior experiences were accounted for. Additionally, the initially positive association between late faculty socialization and year-4 faculty career interest became non-significant after conditioning on baseline interest. Consistent with prior studies (Lopes et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; St. Clair et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this finding suggests that later socialization may reflect continuation of pre-existing orientations\u0026mdash;students who already had higher faculty career interest early on may be more likely to seek out to their advisors\u0026mdash;rather than serving as a distinct driver of changes in career interest.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSTEM doctoral advising is widely recognized as a key feature of graduate training that socialize students into research and academic career pathways (Austin, 2022). Prior work has shown that effective advising encompasses multiple functions (e.g., Paglis et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), yet the literature is less clear on whether their influence is timing-specific. Developmental perspectives (e.g., Bennett \u0026amp; Burke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Weidman et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) suggest that early advising may be particularly consequential because it establishes expectations, routines, and a sense of feasibility during a period of heightened uncertainty, whereas later advising may be less differentiating once trajectories have already been set. Empirically, however, few studies have measured advising multidimensionally at multiple time points and directly tested whether the timing of advising matters for subsequent academic career interest. In this study, we recovered the multidimensional structure of doctoral advising at multiple time points and test whether the timing of advising\u0026mdash;early versus late\u0026mdash;matters for year-4 intentions after accounting for demographics, program context, and baseline interest.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMultidimensional advising across time\u003c/h2\u003e \u003cp\u003eThe evidence advances prior work in several ways. First, we showed that doctoral advising consistently comprises psychosocial support, progress/feedback, networking/political capital, advisor capital/prestige, and publication/funding support, paralleling prior descriptions of advising functions and graduate socialization (Austin \u0026amp; McDaniels, 2006; Weidman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Notably, the item \u0026ldquo;advisor provides information about career paths\u0026rdquo; clustered with progress/feedback in earlier years, but moved to the category of networking by year-4. Such a change suggests that the functional meaning of the same practice may evolve: early career talk is embedded in degree-progress, program structures conversations, while it shifts outward to market navigation and professional communities later (Gardner, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Weidman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). While these patterns in our current study are specific to biology in research intensive contexts in the U.S., they move beyond a generic \u0026ldquo;mentoring matters\u0026rdquo; (Authors, 2024) claim by offering a time-sensitive measurement frame that can be refined and tested for invariance in other fields and institutional settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEarly advising and faculty career interest\u003c/h2\u003e \u003cp\u003eSecond, we applied a block-wise approach to answer our two research questions: a) how do early and late advising relate to year-4 faculty career interest? and b) do associations of early and late advising with year-4 faculty career interest persist after controlling for starting plans? We found that early, structured progress/feedback practices and early scholarly production are the clearest predictors of later faculty interest, while later-stage advising adds little once early conditions are in place. In our context, students who secure early, structured feedback and publication experiences are not only \u0026ldquo;ahead\u0026rdquo; chronologically; they might as well better aligned with institutional expectations of being \u0026ldquo;on time\u0026rdquo; for an academic career (Bennett \u0026amp; Burke, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). From a temporal standpoint, the limited association between late advising and faculty interest may reflect the constraints of \u0026ldquo;compressed time,\u0026rdquo; where students receive more intensive career advising only after many key milestones have already passed. In such cases, even well-intentioned late support may leave little room to reorient toward an academic career. Additionally, the significance of early career interest also suggests that early experiences accumulate into expectations about what is possible.\u003c/p\u003e \u003cp\u003eThe positive association between early publication activity year-4 faculty career interest is not surprising, given that multiple studies support the idea that higher publication productivity predicts stronger alignment with, and pursuit of, faculty careers (Gibbs et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tregellas et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This interpretation may help explain why early advising may matter more than later advising. Phase-based models of doctoral socialization (Weidman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) hold that students move from anticipatory/adjustment (mastering local norms and requirements) to integration (participating in the research community) and finally professional identification (seeing oneself as a member of the field). In that framework, our two significant early predictors, progress/feedback routines and early scholarly production, are likely the practices that enable movement through the first two phases. Systematic check-ins, milestone mapping, and constructive feedback supply the clarity and scaffolding needed to internalize program standards and to take on scholarly tasks (e.g., writing scholarly manuscripts and publishing), which facilitate participation in the research community. By year-4, students who had that early structure are more likely to have accumulated experiences that consolidate an academic identity, which shows up as higher faculty career interest.\u003c/p\u003e \u003cp\u003eSimilarly, the Social Cognitive Career Theory (SCCT; Lent et al., 1994) specifies that mastery experiences are the strongest drivers of self-efficacy, which, together with outcome expectations, shapes interests and goals. Students\u0026rsquo; early publication measure is a direct proxy for mastery experiences, and the process of drafting, revising, submitting, and getting accepted are clear signals of self-efficacy. Stronger efficacy beliefs in a given domain are expected to foster interest in the career path that requires and rewards those competencies. Accordingly, higher self-efficacy in conducting research should be relevant to faculty research careers, where scholarly production is central to role performance and advancement (Lindahl et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stoilescu \u0026amp; McDougall, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). On the other hand, early progress/feedback plays a complementary SCCT role by creating conditions in which mastery experiences can occur. Students not only receive structured encouragement and actionable critique but also get to the stage of having something to write/submit. As they produce early publication, students see that scholarly effort can yield tangible returns, making the academic role appear instrumentally rewarding. Together, early structured feedback and early scholarly production strengthen both self-efficacy and outcome expectations, which together are associated with stronger subsequent interest in research-intensive faculty careers.\u003c/p\u003e \u003cp\u003eLastly, our findings should be read as guidance for aligning advising practices with students\u0026rsquo; goals, not as advocacy for faculty careers. Our outcome of interest\u0026mdash;faculty career interest\u0026mdash;does not imply a normative ranking of careers. Faculty intention is treated as an analytically convenient indicator of one major branch point because it is salient in doctoral culture (Gibbs \u0026amp; Griffin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), highly advisor-sensitive (Sherman et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and widely used in national reporting such as the Survey of Earned Doctorates (NCSES, 2025). Our goal is to illuminate how advising timing relates to the development of diverse career interests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications and future directions\u003c/h2\u003e \u003cp\u003eThis finding extends prior advising research (Authors, 2024; Lind\u0026eacute;n et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) by pinpointing when and what advising matters the most. The results indicate that advising is most consequential when it is structured early and purposefully oriented later. Early in the program, advising that routinizes progress via clear milestones, scheduled check-ins with written action items, and explicit navigation of degree requirements, appears to create the conditions under which students engage in substantive scholarly work. When paired with scaffolded opportunities for publication, such as writing groups and research collaboration (Kwan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Author et al et al., 2025; McGrail et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), these practices convert effort into mastery experiences that are plausibly related to subsequent faculty career interest.\u003c/p\u003e \u003cp\u003eBy contrast, our models indicate that, at least for this dataset, late-stage advising showed little unique association with year-4 interest once early structures and early scholarly production were accounted for. We do not interpret this as evidence that late advising is unimportant. Rather, it suggests a potential practice-intent gap: late advising, in its current form, may be oriented toward administrative clearance or general encouragement rather than targeted conversations or experiences that could plausibly shift students\u0026rsquo; career interests. On the other hand, prior work (Shermann et al., 2021) suggests that as doctoral training progresses, students\u0026rsquo; interests increasingly diversify into a range of academic and nonacademic pathways. It is possible that late‑stage advising still matter, but its effects are likely distributed across diverse pathways instead of concentrated on strengthening faculty‑career interest. Future research could therefore examine how late-stage advising relates to students\u0026rsquo; career interests in nonfaculty academic roles.\u003c/p\u003e \u003cp\u003eTo better support and prepare STEM students for diverse career trajectories, programs can respond by embedding late-stage professional development within existing structures and aligning delivery, content, and incentives with doctoral realities. For example, the Opportunities for Professional Training in Occupations for Scientists (OPTIONS) program developed by Johns Hopkins University is an explicit model of embedding multi‑phase career readiness into doctoral education (Neely et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It offers late‑stage experiential learning such as internships and advising that expand students\u0026rsquo; professional network before graduation. Importantly, the delivery of this type of practice should be feasible for busy graduate students and is encouraged to implemented through asynchronous preparation with repeated offerings across the term, or organized as default-in components connected to routine milestones. To assess whether these adjustments matter, programs can track simple indicators, such as the share of mid-program undecided students who, after late advising, report a concrete career plan. Future research can utilize mixed methods designs to capture how students and advisors enact these routines and to evaluate these interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study advances a developmental account of doctoral advising by showing the diverse effects of early and late advising in predicting faculty interest upon graduation. These findings shift the conversation away from a universal assessment of \u0026ldquo;good\u0026rdquo; advising, and suggest that advising should be understood as a time-sensitive developmental process whose functions may differ across stages of doctoral training.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used de-identified secondary data from the Early Career Research (ECR) project (Feldon et al., 2023), which is funded by the National Science Foundation [#1431234, 1431290, 1760894] and is publicly available via the Open Science Framework (https://osf.io/hymus/). For the present study, an exempt determination for secondary data analysis was granted by the authors’ institutional review board, as the dataset contains no identifiable information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAustin, A. E. (2002). Preparing the next generation of faculty: Graduate school as socialization to the academic career. \u003cem\u003eThe Journal of Higher Education\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(1), 94-122. \u003c/li\u003e\n\u003cli\u003eAustin, P. C., White, I. R., Lee, D. S., \u0026amp; van Buuren, S. (2021). Missing data in clinical research: a tutorial on multiple imputation. \u003cem\u003eCanadian Journal of Cardiology, 37\u003c/em\u003e(9), 1322-1331. \u003c/li\u003e\n\u003cli\u003eBaker, V. L., \u0026amp; Pifer, M. J. (2011). The role of relationships in the transition from doctoral student to independent scholar. \u003cem\u003eStudies in Continuing Education\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 5-17. \u003c/li\u003e\n\u003cli\u003eBennett, A., \u0026amp; Burke, P. J. (2018). Re/conceptualising time and temporality: An exploration of time in higher education. \u003cem\u003eDiscourse: Studies in the Cultural Politics of Education\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(6), 913-925. \u003c/li\u003e\n\u003cli\u003eBodner, T. E. (2008). What improves with increased missing data imputations?. \u003cem\u003eStructural Equation Modeling: A Multidisciplinary Journal, 15\u003c/em\u003e(4), 651-675. \u003c/li\u003e\n\u003cli\u003eChang, C. N., Patterson, C. A., Vanderford, N. L., \u0026amp; Evans, T. M. (2021). Modeling individual development plans, mentoring support, and career preparedness relationships among doctor of philosophy (Ph. D.) trainees in the life sciences. \u003cem\u003eF1000Research\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 626. \u003c/li\u003e\n\u003cli\u003eCharlesworth, T. E., \u0026amp; Banaji, M. R. (2019). Gender in science, technology, engineering, and mathematics: Issues, causes, solutions. \u003cem\u003eJournal of Neuroscience\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(37), 7228-7243. \u003c/li\u003e\n\u003cli\u003eCorn\u0026eacute;r, S., L\u0026ouml;fstr\u0026ouml;m, E., \u0026amp; Pyh\u0026auml;lt\u0026ouml;, K. (2017). The relationships between doctoral students\u0026rsquo; perceptions of supervision and burnout. \u003cem\u003eInternational Journal of Doctoral Studies,\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003e12\u003c/em\u003e, 91-106.\u003c/li\u003e\n\u003cli\u003eDe Welde, K., \u0026amp; Laursen, S. L. (2008). The \u0026ldquo;ideal type\u0026rdquo; advisor: How advisors help STEM graduate students find their \u0026lsquo;scientific feet\u0026rsquo;. \u003cem\u003eThe Open Education Journal\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 49-61. \u003c/li\u003e\n\u003cli\u003eDericks, G., Thompson, E., Roberts, M., \u0026amp; Phua, F. (2019). Determinants of PhD student satisfaction: the roles of supervisor, department, and peer qualities. \u003cem\u003eAssessment \u0026amp; Evaluation in Higher Education.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eGanning, J. (2024). Doctoral education and the academic job market in planning. \u003cem\u003eJournal of Planning Education and Research\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(3), 1063-1077. \u003c/li\u003e\n\u003cli\u003eGardner, S. K. (2009). The Development of Doctoral Students--Phases of Challenge and Support. \u003cem\u003eASHE Higher Education Report, 34\u003c/em\u003e(6), 1-127. \u003c/li\u003e\n\u003cli\u003eGardner, S. K., \u0026amp; Mendoza, P. (Eds.). (2023). On becoming a scholar: Socialization and development in doctoral education. Taylor \u0026amp; Francis. \u003c/li\u003e\n\u003cli\u003eGerman, K. T., Sweeny, K., \u0026amp; Robbins, M. L. (2019). Investigating the role of the faculty advisor in doctoral students\u0026rsquo; career trajectories. \u003cem\u003eProfessional Development in Education\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(5), 762-773. \u003c/li\u003e\n\u003cli\u003eGibbs Jr, K. D., \u0026amp; Griffin, K. A. (2013). What do I want to be with my PhD? The roles of personal values and structural dynamics in shaping the career interests of recent biomedical science PhD graduates. \u003cem\u003eCBE---Life Sciences Education\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 711-723. \u003c/li\u003e\n\u003cli\u003eGibbs Jr, K. D., McGready, J., Bennett, J. C., \u0026amp; Griffin, K. (2014). Biomedical science Ph. D. career interest patterns by race/ethnicity and gender. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(12), e114736. \u003c/li\u003e\n\u003cli\u003eGinther, D. K. (2021). Gender, race, and academic career outcomes: Does economics mirror other disciplines?. \u003cem\u003eNBER Reporter\u003c/em\u003e, (3), 22-26.\u003c/li\u003e\n\u003cli\u003eGraham, J. W., Olchowski, A. E., \u0026amp; Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. \u003cem\u003ePrevention Science, 8\u003c/em\u003e(3), 206-213. \u003c/li\u003e\n\u003cli\u003eHu, D., Park, H. J., Ruberton, P. M., Smyth, J. M., Cohen, G. L., Purdie-Greenaway, V., \u0026amp; Cook, J. E. (2026). Trust in advisor predicts Ph. D. students\u0026apos; academic motivation, well-being, and achievement: A prospective longitudinal study. \u003cem\u003ePNAS nexus, 5\u003c/em\u003e(1), pgaf411. \u003c/li\u003e\n\u003cli\u003eIbarra, H., Carter, N. M., \u0026amp; Silva, C. (2010). Why men still get more promotions than women. \u003cem\u003eHarvard Business Review\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(9), 80-85. \u003c/li\u003e\n\u003cli\u003eJennrich, R. I., \u0026amp; Sampson, P. F. (1966). Rotation for simple loadings. \u003cem\u003ePsychometrika, 31\u003c/em\u003e(3), 313-323. \u003c/li\u003e\n\u003cli\u003eJeong, S., Blaney, J. M., \u0026amp; Feldon, D. F. (2019). Identifying faculty and peer interaction patterns of first-year biology doctoral students: A latent class analysis. \u003cem\u003eCBE---Life Sciences Education\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), ar59. \u003c/li\u003e\n\u003cli\u003eKish, L. (1965). Survey sampling. \u003c/li\u003e\n\u003cli\u003eKwan, P. P., Sharp, S., Mason, S., \u0026amp; Saetermoe, C. L. (2021). Faculty writing groups: The impact of protected writing time and group support. \u003cem\u003eInternational Journal of Educational Research Open\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 100100.\u003c/li\u003e\n\u003cli\u003eLane, M., Dooley, K., Cavu, K., \u0026amp; Jaatinen, E. (2025). Aspiration versus outcome: the career intentions of PhD students in an Australian university. \u003cem\u003eStudies in Graduate and Postdoctoral Education\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLindahl, J., Colliander, C., \u0026amp; Danell, R. (2021). The importance of collaboration and supervisor behaviour for gender differences in doctoral student performance and early career development. \u003cem\u003eStudies in Higher Education\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(12), 2808-2831.\u003c/li\u003e\n\u003cli\u003eLind\u0026eacute;n, J., Ohlin, M., \u0026amp; Brodin, E. M. (2013). Mentorship, supervision and learning experience in PhD education. \u003cem\u003eStudies in Higher Education\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(5), 639-662. \u003c/li\u003e\n\u003cli\u003eLopes, J., Ranieri, V., Lambert, T., Pugh, C., Barratt, H., Fulop, N. J., ... \u0026amp; Best, D. (2017). The clinical academic workforce of the future: a cross-sectional study of factors influencing career decision-making among clinical PhD students at two research-intensive UK universities. \u003cem\u003eBMJ open\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(8), e016823. \u003c/li\u003e\n\u003cli\u003eMaher, M. A., Wofford, A. M., Roksa, J., \u0026amp; Feldon, D. F. (2020). Finding a fit: Biological science doctoral students\u0026rsquo; selection of a principal investigator and research laboratory. \u003cem\u003eCBE\u0026mdash;Life Sciences Education\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3), ar31.\u003c/li\u003e\n\u003cli\u003eMansson, D. H., \u0026amp; Myers, S. A. (2012). Using mentoring enactment theory to explore the doctoral student\u0026ndash;advisor mentoring relationship. \u003cem\u003eCommunication Education\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(4), 309-334. \u003c/li\u003e\n\u003cli\u003eMcGrail, M. R., Rickard, C. M., \u0026amp; Jones, R. (2006). Publish or perish: A systematic review of interventions to increase academic publication rates. \u003cem\u003eHigher Education Research \u0026amp; Development\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 19-35.\u003c/li\u003e\n\u003cli\u003eNational Academies of Sciences, Engineering, and Medicine. (2025). Reimagining STEMM Graduate Education and Postdoctoral Career Development: Proceedings of a Summit---in Brief. \u003c/li\u003e\n\u003cli\u003eNational Center for Science and Engineering Statistics (NCSES). 2025. \u003cem\u003eDoctorate Recipients from U.S. Universities: 2024 Data Tables\u003c/em\u003e. NSF 25-349. Alexandria, VA: U.S. National Science Foundation. Available at https://ncses.nsf.gov/surveys/earned-doctorates/2024. \u003c/li\u003e\n\u003cli\u003eNational Institutes of Health. (2019). \u003cem\u003eStrengthening the biomedical research workforce\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eNeely, C. J., Abras, C., Lauka, B., Oladeinde, M., \u0026amp; Eith, C. A. (2025). Bridging the gap: the OPTIONS program as a model for integrating career development into biomedical PhD training. In \u003cem\u003eFrontiers in Education\u003c/em\u003e (Vol. 10, p. 1478553). Frontiers Media SA.\u003c/li\u003e\n\u003cli\u003eNersesian, P. V., Starbird, L. E., Wilson, D. M., Marea, C. X., Uveges, M. K., Choi, S. S. W., ... \u0026amp; Cajita, M. I. (2019). Mentoring in research-focused doctoral nursing programs and student perceptions of career readiness in the United States. \u003cem\u003eJournal of Professional Nursing\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(5), 358-364. \u003c/li\u003e\n\u003cli\u003eNoe, R. A. (1988). An investigation of the determinants of successful assigned mentoring relationships. \u003cem\u003ePersonnel Psychology, 41\u003c/em\u003e(3), 457-479. \u003c/li\u003e\n\u003cli\u003ePaglis, L. L., Green, S. G., \u0026amp; Bauer, T. N. (2006). Does adviser mentoring add value? A longitudinal study of mentoring and doctoral student outcomes. \u003cem\u003eResearch in Higher Education\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(4), 451-476. \u003c/li\u003e\n\u003cli\u003ePifer, M. J., \u0026amp; Baker, V. L. (2016). Stage-based challenges and strategies for support in doctoral education: A practical guide for students, faculty members, and program administrators. \u003cem\u003eInternational Journal of Doctoral Studies\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 15. \u003c/li\u003e\n\u003cli\u003ePinheiro, D. L., Melkers, J., \u0026amp; Newton, S. (2017). Take me where I want to go: Institutional prestige, advisor sponsorship, and academic career placement preferences. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), e0176977. \u003c/li\u003e\n\u003cli\u003ePodsakoff, P. M., MacKenzie, S. B., Lee, J. Y., \u0026amp; Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. \u003cem\u003eJournal of Applied Psychology\u003c/em\u003e, \u003cem\u003e88\u003c/em\u003e(5), 879. \u003c/li\u003e\n\u003cli\u003ePortnoi, L. M., Chlopecki, A. L. A., \u0026amp; Peregrina-Kretz, D. (2015). Expanding the doctoral student socialization framework: The central role of student agency. \u003cem\u003eThe Journal of Faculty Development\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 5-16. \u003c/li\u003e\n\u003cli\u003eRoach, M., \u0026amp; Sauermann, H. (2017). The declining interest in an academic career. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(9), e0184130. \u003c/li\u003e\n\u003cli\u003eSarrico, C. S. (2022). The expansion of doctoral education and the changing nature and purpose of the doctorate. \u003cem\u003eHigher Education\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(6), 1299-1315. \u003c/li\u003e\n\u003cli\u003eSauermann, H., \u0026amp; Roach, M. (2012). Science PhD career preferences: levels, changes, and advisor encouragement. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(5), e36307. \u003c/li\u003e\n\u003cli\u003eScalo, J., \u0026amp; Freauff, L. (2020). \u003cem\u003eEvaluation of the National Institutes of Health (NIH) Broadening Experiences in Scientific Training (BEST) Program\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eSedgwick, P. (2012). What is recall bias?. \u003cem\u003eBmj\u003c/em\u003e, \u003cem\u003e344\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eSherman, D. K., Ortosky, L., Leong, S., Kello, C., \u0026amp; Hegarty, M. (2021). The changing landscape of doctoral education in science, technology, engineering, and mathematics: PhD students, faculty advisors, and preferences for varied career options. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 711615. \u003c/li\u003e\n\u003cli\u003eSkakni, I., Kereselidze, N., Parmentier, M., Delobbe, N., \u0026amp; Inouye, K. (2025). PhD graduates pursuing careers beyond academia: a scoping review. \u003cem\u003eHigher Education Research \u0026amp; Development\u003c/em\u003e, 1-20. \u003c/li\u003e\n\u003cli\u003eSkinnider, M. A., Twa, D. D., Squair, J. W., Rosenblum, N. D., Lukac, C. D., \u0026amp; Canadian MD/PhD Program Investigation Group. (2018). Predictors of sustained research involvement among MD/PhD programme graduates. \u003cem\u003eMedical Education\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(5), 536-545. \u003c/li\u003e\n\u003cli\u003eSt. Clair, R., Hutto, T., MacBeth, C., Newstetter, W., McCarty, N. A., \u0026amp; Melkers, J. (2017). The \u0026ldquo;new normal\u0026rdquo;: Adapting doctoral trainee career preparation for broad career paths in science. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), e0177035. \u003c/li\u003e\n\u003cli\u003eStoilescu, D., \u0026amp; McDougall, D. (2010). Starting to publish academic research as a doctoral student. \u003cem\u003eInternational Journal of Doctoral Studies\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e\u003cem\u003e 5\u003c/em\u003e, 79-92.\u003c/li\u003e\n\u003cli\u003eTen Berge, J. M., Krijnen, W. P., Wansbeek, T., \u0026amp; Shapiro, A. (1999). Some new results on correlation-preserving factor scores prediction methods. \u003cem\u003eLinear Algebra and Its Applications, 289\u003c/em\u003e(1-3), 311-318. \u003c/li\u003e\n\u003cli\u003eTregellas, J. R., Smucny, J., Rojas, D. C., \u0026amp; Legget, K. T. (2018). Predicting academic career outcomes by predoctoral publication record. \u003cem\u003ePeerJ, 6\u003c/em\u003e, e5707.\u003c/li\u003e\n\u003cli\u003eWapman, K. H., Zhang, S., Clauset, A., \u0026amp; Larremore, D. B. (2022). Quantifying hierarchy and dynamics in US faculty hiring and retention. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e610\u003c/em\u003e(7930), 120-127. \u003c/li\u003e\n\u003cli\u003eWeidman, J. C., Twale, D. J., \u0026amp; Stein, E. L. (2001). \u003cem\u003eSocialization of Graduate and Professional Students in Higher Education: A Perilous Passage? ASHE-ERIC Higher Education Report, Volume 28, Number 3. Jossey-Bass Higher and Adult Education Series\u003c/em\u003e. Jossey-Bass, Publishers, Inc., 350 Sansome Street, San Francisco, CA 94104-1342. \u003c/li\u003e\n\u003cli\u003eWest, M., McCain, J., \u0026amp; Roksa, J. (2024). After the PhD: the role of advisors and social connections in the job search process. \u003cem\u003eStudies in Graduate and Postdoctoral Education\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 380-394. \u003c/li\u003e\n\u003cli\u003eWood, C. V., Jones, R. F., Remich, R. G., Caliendo, A. E., Langford, N. C., Keller, J. L., ... \u0026amp; McGee, R. (2020). The National Longitudinal Study of Young Life Scientists: Career differentiation among a diverse group of biomedical PhD students. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6), e0234259.\u003c/li\u003e\n\u003cli\u003eYang, Y., \u0026amp; Cai, J. (2022). Profiles of PhD students\u0026rsquo; satisfaction and their relationships with demographic characteristics and academic career enthusiasm. \u003cem\u003eFrontiers in Psychology, 13\u003c/em\u003e, 968541.\u003c/li\u003e\n\u003cli\u003eZhao, C. M., Golde, C. M., \u0026amp; McCormick, A. C. (2007). More than a signature: How advisor choice and advisor behaviour affect doctoral student satisfaction. \u003cem\u003eJournal of Further and Higher education, 31\u003c/em\u003e(3), 263-281.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Doctoral education, Advising, Timing, Career interest, Socialization","lastPublishedDoi":"10.21203/rs.3.rs-9051283/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9051283/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Doctoral advising is widely recognized as a key feature of graduate training that socializes students into research and academic career pathways. While advising is critical for career development, we know little about when advising matters the most across the doctoral trajectory. This longitudinal study investigated whether early versus late advising differentially predict faculty career interest among 330 biology doctoral students across 53 U.S. research universities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Using exploratory factor analysis and block-wise ordinal regression, we found that early degree progress/feedback advising significantly predicted higher faculty career interest in year-4, even after controlling baseline intentions. By contrast, late advising factors added no incremental explanatory power once early experiences were accounted for.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Findings suggest advising is most consequential when it provides structured progress feedback and scaffolds early scholarly production. Results support stage-based socialization theories and suggest STEM doctoral programs should embed career-relevant advising earlier.\u003c/p\u003e","manuscriptTitle":"When Advising Matters: Early Versus Late Faculty Advising and STEM Doctoral Students' Faculty Career Interests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 08:37:38","doi":"10.21203/rs.3.rs-9051283/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66326fcc-cc92-4bd5-bc5b-f4ba19cfa439","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T08:37:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 08:37:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9051283","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9051283","identity":"rs-9051283","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}