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Building Transfer Student Interest in Computer Science PhDs: Examining an Advising Intervention Using a Staged Innovation Design

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Title: Building Transfer Student Interest in Computer Science PhDs: Examining an Advising Intervention Using a Staged Innovation Design
Language: English
Authors: Jennifer M. Blaney (ORCID 0000-0002-9146-6425), David F. Feldon (ORCID 0000-0003-3268-5764), Annie M. Wofford (ORCID 0000-0002-2246-1946), Kaylee Litson (ORCID 0000-0003-1296-4811)
Source: Research in Higher Education. 2025 66(4).
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 24
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 2439166
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Two Year Colleges
Descriptors: College Transfer Students, Community College Students, Graduate Study, Access to Education, Computer Science Education, Student Interests, Intervention, Predictor Variables
DOI: 10.1007/s11162-025-09843-9
ISSN: 0361-0365
1573-188X
Abstract: Community college transfer students represent a diverse and talented group to recruit to PhD and other graduate programs. Yet, little is known about practical strategies to support community college transfer students' access to graduate training. Focusing specifically on transfer students in computer science and guided by social cognitive career theory, this manuscript draws on survey data from over 200 community college transfer students and utilizes a staged innovation design to examine a new intervention designed to pique transfer students' interests in PhD study. Findings suggest that brief targeted interventions can significantly predict transfer students' perceptions about PhD study, but that more sustained efforts will likely be necessary to influence transfer students' more tangible degree plans. In addition to highlighting implications for future research, we identify strategies for faculty and staff seeking to support community college transfer students and build access to graduate training.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1471269
Database: ERIC
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  Value: <anid>AN0185703143;rhe01jun.25;2025Jun18.13:04;v2.2.500</anid> <title id="AN0185703143-1">Building Transfer Student Interest in Computer Science PhDs: Examining an Advising Intervention Using a Staged Innovation Design </title> <p>Community college transfer students represent a diverse and talented group to recruit to PhD and other graduate programs. Yet, little is known about practical strategies to support community college transfer students' access to graduate training. Focusing specifically on transfer students in computer science and guided by social cognitive career theory, this manuscript draws on survey data from over 200 community college transfer students and utilizes a staged innovation design to examine a new intervention designed to pique transfer students' interests in PhD study. Findings suggest that brief targeted interventions can significantly predict transfer students' perceptions about PhD study, but that more sustained efforts will likely be necessary to influence transfer students' more tangible degree plans. In addition to highlighting implications for future research, we identify strategies for faculty and staff seeking to support community college transfer students and build access to graduate training.</p> <p>The importance of faculty diversity has been broadly recognized within research on equity in science, technology, engineering, and mathematics (STEM) programs, as a diverse professoriate is critical to supporting broader diversity and equity efforts across campus (Cross & Carman, [<reflink idref="bib25" id="ref1">25</reflink>]; Phillips, [<reflink idref="bib66" id="ref2">66</reflink>]; Stout et al., [<reflink idref="bib76" id="ref3">76</reflink>]). Efforts to expand access to STEM faculty pathways are especially vital in computer science (CS), which is one of the least diverse STEM fields, with women comprising 22% and Black, Latinx, and Indigenous students combined making up less than 4% of doctoral recipients (Zweben & Bizot, [<reflink idref="bib92" id="ref4">92</reflink>]). Further, CS is experiencing a shortage of PhDs to fill faculty positions. Coupled with growing undergraduate interest in CS majors, this faculty shortage leads many programs to adopt competitive enrollment practices (e.g., prerequisite requirements to access the major), which further exacerbate inequity in the field (Nguyen & Lewis, [<reflink idref="bib61" id="ref5">61</reflink>]). Thus, there is an urgent need to increase the number of and diversity among students following PhD pathways in CS, so that programs can fill open faculty positions and adopt inclusive policies to support all students interested in pursuing CS degrees.</p> <p>One way to effectively expand and diversify the CS professoriate is to develop a pathway from community colleges to doctoral programs. In addition to being a particularly high-achieving group (Carlan & Byxbe, [<reflink idref="bib19" id="ref6">19</reflink>]; Xu et al., [<reflink idref="bib91" id="ref7">91</reflink>]), community college transfer students tend to be more diverse than other STEM students in terms of race, ethnicity, parental education, and socioeconomic status (Blaney, [<reflink idref="bib4" id="ref8">4</reflink>]). Further, some evidence suggests that transfer students in CS disproportionately bring PhD interests with them to their receiving universities (Blaney & Wofford, [<reflink idref="bib6" id="ref9">6</reflink>], [<reflink idref="bib7" id="ref10">7</reflink>]). Unfortunately, transfer students face myriad barriers that may discourage them from pursuing their academic interests (Blaney et al., [<reflink idref="bib11" id="ref11">11</reflink>]). More research is needed to identify how receiving institutions can bolster tangible support structures to reduce barriers for transfer students, ultimately helping them realize their PhD interests.</p> <p>Guided by social cognitive career theory (Lent et al., [<reflink idref="bib47" id="ref12">47</reflink>]) and Wang's ([<reflink idref="bib82" id="ref13">82</reflink>], [<reflink idref="bib83" id="ref14">83</reflink>]) work on STEM community college transfer pathways (i.e., vertical transfer), we implemented a multi-phase intervention to foster PhD interests among vertical transfer CS majors, using a staged innovation design (Clark & Snow, [<reflink idref="bib23" id="ref15">23</reflink>]) to examine its role in predicting key outcomes. Specifically, we developed and implemented a session offered during orientation for new transfer students, created tailored advising materials, and provided professional development for academic advisors to pique new transfer students' interest in PhDs. This paper relies on longitudinal survey data from <emph>N</emph> = 223 vertical transfer CS majors, <emph>n</emph> = 145 of whom experienced the intervention and <emph>n</emph> = 78 of whom were in a control group. We assessed changes in PhD beliefs and interests associated with exposure to our intervention, while also considering variation by incoming characteristics and identities. The following questions guide this inquiry:</p> <p></p> <ulist> <item> Does participation in the intervention predict vertical transfer students' PhD perceptions, graduate school plans, and PhD interests?</item> <p></p> <item> Do the outcomes associated with the intervention differ by gender, race/ethnicity, first-generation college status, or socioeconomic status?</item> </ulist> <hd id="AN0185703143-2">Guiding Literature</hd> <p>This study is guided by literature on graduate school aspirations among STEM students and studies of vertical transfer student success. In the sections that follow, after discussing the literature on pathways to graduate study in STEM, we highlight studies that consider vertical transfer in computing[<reflink idref="bib1" id="ref16">1</reflink>] majors, followed by studies that focus on community college pathways to graduate degrees.</p> <hd id="AN0185703143-3">Aspirations and Pathways to Graduate School in STEM Disciplines</hd> <p>Although undergraduate students' experiences in STEM disciplines have long been a prominent focus of higher education research, subsequent pathways to and through STEM graduate school have largely come into focus over the last two decades (Austin, [<reflink idref="bib1" id="ref17">1</reflink>]; Austin & McDaniels, [<reflink idref="bib2" id="ref18">2</reflink>]; Feldon et al., [<reflink idref="bib31" id="ref19">31</reflink>]; National Academies of Science, Engineering, and Medicine [NASEM], [<reflink idref="bib60" id="ref20">60</reflink>]). Across all disciplines, extant studies have documented individual, relational, and structural factors that shape students' aspirations for earning a graduate degree, often focusing on the important role of early pre-college aspirations (e.g., Carlton, [<reflink idref="bib20" id="ref21">20</reflink>]; English & Umbach, [<reflink idref="bib30" id="ref22">30</reflink>]; Hanson et al., [<reflink idref="bib36" id="ref23">36</reflink>]; Nicole & DeBoer, [<reflink idref="bib63" id="ref24">63</reflink>]). Early graduate degree aspirations predict graduate school pursuits in STEM (Xu, [<reflink idref="bib90" id="ref25">90</reflink>]), and scholars have noted the important role collegiate experiences (e.g., undergraduate research opportunities) can play in further developing students' aspirations and intentions (Szelényi & Inkelas, [<reflink idref="bib77" id="ref26">77</reflink>]). For example, Eagan and colleagues' ([<reflink idref="bib27" id="ref27">27</reflink>]) found that undergraduate research participation increased the likelihood of students' intentions to pursue STEM graduate degrees by approximately 14 to 17% points, suggesting unique value to these research experiences. Further, co-curricular STEM opportunities (e.g., undergraduate research) may be even more crucial for students who have been minoritized in STEM (Lane, [<reflink idref="bib43" id="ref28">43</reflink>]; López et al., [<reflink idref="bib51" id="ref29">51</reflink>]; Pender et al., [<reflink idref="bib65" id="ref30">65</reflink>]), as these students may not have had access to the same information about research and graduate training as their peers from privileged backgrounds.</p> <p>Indeed, minoritization in STEM also shapes who holds graduate school aspirations (NASEM, [<reflink idref="bib60" id="ref31">60</reflink>]). STEM disciplines and graduate education—as well as the nexus between these environments—have historically operated in the interests of students who are white, cisgender men, are from higher-income backgrounds, and have college-educated parents (e.g., Fleming et al., [<reflink idref="bib32" id="ref32">32</reflink>]; NASEM, [<reflink idref="bib60" id="ref33">60</reflink>]; Posselt, [<reflink idref="bib68" id="ref34">68</reflink>]). Advancing equitable pathways for students who are racially minoritized, women, non-binary, trans, low-income, and/or first-generation to college requires that faculty and administrators consider how discriminatory norms (e.g., hierarchical power dynamics, individualism) shape STEM graduate pathways for students with minoritized identities (Posselt, [<reflink idref="bib68" id="ref35">68</reflink>]). Researchers have shown that accessible, trusting advisor relationships are particularly crucial for women, racially minoritized, and first-generation students' successful navigation of pathways to and through STEM graduate school (Bryson et al., [<reflink idref="bib16" id="ref36">16</reflink>]; Burt et al., [<reflink idref="bib17" id="ref37">17</reflink>]; Wofford & Blaney, [<reflink idref="bib6" id="ref38">6</reflink>]; Wofford et al., [<reflink idref="bib88" id="ref39">88</reflink>]). According to Wilkins-Yel and colleagues ([<reflink idref="bib85" id="ref40">85</reflink>]), graduate advisors hold the power to provide support and advocacy, which can disrupt structures of inequity and power dynamics that may uniquely affect students with multiple minoritized identities. Further, given how the "hidden curriculum"—or informal, unwritten rules for success—operates to influence success in navigating graduate school pathways (within and beyond STEM), it is important to remember that first-generation and low-income students may not have as much access to accurate information about graduate degrees as their peers with college-educated parents or from affluent backgrounds (Rol & Bukoski, [<reflink idref="bib70" id="ref41">70</reflink>]; Signal et al., [<reflink idref="bib72" id="ref42">72</reflink>]; Wofford et al., [<reflink idref="bib88" id="ref43">88</reflink>]).</p> <p>In computing fields more specifically, a growing body of work has pointed to specific factors that influence students' graduate school pathways as well as for whom those factors matter most (e.g., Blaney & Wofford, [<reflink idref="bib6" id="ref44">6</reflink>]; Charleston, [<reflink idref="bib22" id="ref45">22</reflink>]; Cohoon et al., [<reflink idref="bib24" id="ref46">24</reflink>]; Wofford, [<reflink idref="bib87" id="ref47">87</reflink>]; Wofford et al., [<reflink idref="bib89" id="ref48">89</reflink>]). Computing fields remain some of the most homogenous and exclusionary (NCSES, [<reflink idref="bib59" id="ref49">59</reflink>]), shaping <emph>which</emph> and <emph>how</emph> students gain knowledge about and take action toward post-baccalaureate educational or career opportunities. However, psychosocial attributes (e.g., self-confidence, self-efficacy, computing identity) have emerged as a crucial set of factors predicting undergraduate students' aspirations for computing graduate degrees (Wofford, [<reflink idref="bib87" id="ref50">87</reflink>]; Wofford et al., [<reflink idref="bib89" id="ref51">89</reflink>]). These attributes are malleable and context dependent, indicating that interventions targeting both individuals and learning environments can potentially support and positively influence students' aspirations.</p> <p>Wofford ([<reflink idref="bib87" id="ref52">87</reflink>]) found that women's self-confidence for gaining admission to computing graduate school decreased relative to men's self-confidence over two years of undergraduate experiences in computing, with several environmental factors indirectly contributing to such a decrease. Further, some evidence suggests that Black and Latine students are more likely than their white peers to develop graduate aspirations in computing (Wofford et al., [<reflink idref="bib89" id="ref53">89</reflink>]); yet, these aspirations are often left unrealized in the makeup of newly enrolled computing graduate students. Together, these domain-specific results in computing fields paint a complex picture in which minoritized students receive insufficient institutional support for earning graduate degrees, leaving significant room for equity-minded changes to departmental support structures.</p> <hd id="AN0185703143-4">Vertical Transfer Student Success</hd> <p>Prior research has emphasized the potential community college transfer pathways hold for diversifying STEM fields (Bahr et al., [<reflink idref="bib3" id="ref54">3</reflink>]), with recent studies exploring the demographic composition of vertical transfer aspirants (Blaney et al., [<reflink idref="bib8" id="ref55">8</reflink>]) and successful transfer students (Blaney, [<reflink idref="bib4" id="ref56">4</reflink>]) in CS and related majors. For instance, using national survey data, researchers have found that computing majors who transfer from community colleges are more racially and socioeconomically diverse than those who enter their computing majors directly from high school (Blaney, [<reflink idref="bib4" id="ref57">4</reflink>]). Unfortunately, other studies highlight the myriad barriers (e.g., poor transfer advising, credit loss, etc.) that vertical transfer aspirants face in their degree pursuits (Hartman, [<reflink idref="bib38" id="ref58">38</reflink>]), particularly when they seek access to competitive STEM majors (Denner et al., [<reflink idref="bib26" id="ref59">26</reflink>]; Holland Zahner, [<reflink idref="bib40" id="ref60">40</reflink>]; Lyon & Denner, [<reflink idref="bib53" id="ref61">53</reflink>]).</p> <p>Additional prior research explores the pathways and successes of transfer students <emph>after</emph> they matriculate into university programs. While early literature on this topic tended to focus on transfer shock and adjustment (e.g., Hills, [<reflink idref="bib39" id="ref62">39</reflink>]), more recent studies have refocused on the responsibility of receiving universities to develop receptive transfer environments (Elliott & Lakin, [<reflink idref="bib29" id="ref63">29</reflink>]; Jain et al., [<reflink idref="bib41" id="ref64">41</reflink>]; Umbach et al., [<reflink idref="bib78" id="ref65">78</reflink>]). In CS, transfer student experiences are also characterized by larger patriarchal environments, in which women transfer students may encounter disproportionate stigma for having attended community college, greater obstacles navigating their receiving universities (Blaney, Hernandez et al., [<reflink idref="bib10" id="ref66">10</reflink>]), and more limited support from other computing students, relative to men who transfer (Blaney & Barrett, [<reflink idref="bib5" id="ref67">5</reflink>]). However, vertical transfer students in computing also bring a wealth of prior knowledge and resilience with them to their receiving universities (Laanan et al., [<reflink idref="bib75" id="ref68">75</reflink>]), contributing to their high rates of degree completion and post-graduate success (Wang & Wickersham, [<reflink idref="bib84" id="ref69">84</reflink>]), which should be recognized by universities (Blaney & Barrett, [<reflink idref="bib5" id="ref70">5</reflink>]). Collectively, extant literature underscores the critical responsibility of universities to equitably support transfer students' aspirations and capitalize on the assets that transfer students bring with them to their universities.</p> <hd id="AN0185703143-5">Vertical Transfer Pathways to Graduate Study</hd> <p>While fewer studies have considered vertical transfer pathways to PhDs and other graduate programs, researchers have long asked questions about the degree aspirations of community college students (Laanan, [<reflink idref="bib42" id="ref71">42</reflink>]; Starobin & Laanan, [<reflink idref="bib75" id="ref72">75</reflink>]), with recent studies taking a closer look at graduate degree trajectories among transfer students (Blaney et al., [<reflink idref="bib11" id="ref73">11</reflink>]; Blaney & Wofford, [<reflink idref="bib6" id="ref74">6</reflink>]; Fregoso, [<reflink idref="bib34" id="ref75">34</reflink>]). For instance, one recent study used propensity score matching to examine bachelor's degree recipients' pathways to graduate school as a product of prior community college attendance, finding no significant difference between vertical transfer and other college graduates (Wang et al., [<reflink idref="bib84" id="ref76">84</reflink>]). Other research has found particularly frequent PhD aspirations among vertical transfer students in computing, noting that computing self-efficacy and positive interactions with faculty predict sustained graduate school interests over time for vertical transfer students (Blaney & Wofford, [<reflink idref="bib6" id="ref77">6</reflink>]; Blaney, Feldon et al., [<reflink idref="bib9" id="ref78">9</reflink>]). Especially relevant to our study, which focused on academic advising for vertical transfer students, qualitative studies have illustrated how academic advisors can validate transfer students' graduate degree interests and create developmental opportunities to students to prepare for graduate training (Martinez & Elue, [<reflink idref="bib54" id="ref79">54</reflink>]).</p> <p>Unfortunately, scholars have also revealed how universities do not provide transfer students with equitable research and other professional development opportunities that could facilitate their access to graduate training (Elliott & Lakin, [<reflink idref="bib29" id="ref80">29</reflink>]; Solis & Durán, [<reflink idref="bib74" id="ref81">74</reflink>]). In computing specifically, recent research has documented how vertical transfer students may be deterred from pursuing PhDs because of perceived opportunity costs, pressure to enter the workforce as quickly as possible, and limited access to information about PhD training opportunities (Blaney et al., [<reflink idref="bib11" id="ref82">11</reflink>]). In light of these documented barriers to PhD training for vertical transfer students, some scholarship has focused on specific programmatic interventions, which can be effective in preparing transfer students for PhD study (Nguyen et al., [<reflink idref="bib62" id="ref83">62</reflink>]), though PhD-related interventions have not been widely explored for vertical transfer students in computing majors.</p> <hd id="AN0185703143-6">Conceptual Framework</hd> <p>This study is guided by social cognitive career theory (SCCT). SCCT outlines the cognitive and motivational processes that shape career choices and outcomes. Broadly, this framework emphasizes the importance of self-efficacy and outcome expectations in mediating the relationship between experiences and degree/career outcomes (Lent et al., [<reflink idref="bib47" id="ref84">47</reflink>]). Self-efficacy refers to students' beliefs about their own competencies, which govern students' effort investment and willingness to take on challenging goals. Outcome expectations more specifically refer to students' beliefs about the value, utility, and attainability of pursuing a specific goal (e.g., earning a PhD) and are said to predict interest in pursuing a given career in conjunction with personal characteristics, past and present supports, and affordances present in the learning context (Lent et al., [<reflink idref="bib45" id="ref85">45</reflink>]). These factors, mediated by individuals' interests, in turn continue to predict learners' selected goals and pursuit of those goals.</p> <p>SCCT as an overarching framework, and its core components, have been well supported by empirical studies with STEM students. In a meta-analysis of 196 independent samples from STEM domains, self-efficacy and outcome expectations accounted for 64% of the variance in STEM interest (Lent et al., [<reflink idref="bib50" id="ref86">50</reflink>]). In turn, STEM interest explained 36% of the variance in STEM career aspirations. Further, Lent and colleagues ([<reflink idref="bib48" id="ref87">48</reflink>]) demonstrated the fit of SCCT in computing fields across racial/ethnic groups and contexts. More recently, SCCT has been applied to explain both students' decisions to pursue STEM graduate degrees (Borrego et al., [<reflink idref="bib13" id="ref88">13</reflink>]) and the pathways that students follow in pursuit of vertical transfer from a community college into a four-year STEM major (Wang, [<reflink idref="bib82" id="ref89">82</reflink>]).</p> <p>Our study is particularly focused on developing transfer students' outcome expectations for PhD study. In addition to outcome expectations broadly being theorized to inform degree pursuits (Lent et al., [<reflink idref="bib45" id="ref90">45</reflink>]), graduate school outcome expectations have been shown to predict more tangible plans to pursue graduate study (Borrego et al., [<reflink idref="bib13" id="ref91">13</reflink>]). One mechanism through which outcome expectations are shaped and reshaped is the provision of accurate information. For example, Bolkan and colleagues ([<reflink idref="bib12" id="ref92">12</reflink>]) found that prescriptive advising, in which the emphasis rests on providing students with concrete information about requirements and policies needed to attain academic goals, significantly enhanced students' academic beliefs and subsequent graduation rates. Consistent with this pattern, Brown and colleagues' ([<reflink idref="bib15" id="ref93">15</reflink>]) meta-analysis of quantitative studies focusing on educational and career outcomes found that such supports explained almost 17% of the variance in outcome expectations.</p> <p>Applying SCCT, we focus our inquiry on exploring a new intervention that provides vertical transfer students in CS with accurate information about PhD study to counteract any previously held myths or stereotypes about CS doctoral education. We also directly targeted outcome expectations through an exercise intended to help participants envision the utility and value of pursuing a PhD. This strategy targeted students' expectancies and subjective task values as influential factors affecting the pursuit and attainment of academic goals (Eccles & Wigfield, [<reflink idref="bib28" id="ref94">28</reflink>]; Rosenzweig et al., [<reflink idref="bib71" id="ref95">71</reflink>]). The central feature of interventions to improve outcome expectations lies in structuring students' engagement with an activity's utility and value (e.g., the utility and value of pursuing a PhD), often by using an exercise in which students generate for themselves the value and expected outcomes of goal pursuit (Harackiewicz & Priniski, [<reflink idref="bib37" id="ref96">37</reflink>]). Such activities, commonly referred to as "saying-is-believing" interventions, are designed to influence how students perceive expected outcomes of pursuing a specific goal (Harackiewicz & Priniski, [<reflink idref="bib37" id="ref97">37</reflink>]; Rosenzweig et al., [<reflink idref="bib71" id="ref98">71</reflink>]). As such, within our study, we asked participants to briefly write a few sentences on the personal relevance and utility of PhD training and their likelihood of success if they choose to pursue a PhD, which we posited would foster positive PhD outcome expectations and interests. As described below, this "saying-is-believing" activity was one component of a larger intervention designed to pique transfer students' outcome expectations and interests in PhD study.</p> <hd id="AN0185703143-7">Methods</hd> <p>Our research questions ask about the efficacy of an intervention designed to pique CS transfer students' interests in pursuing graduate school, particularly PhDs. We used a three-year staged innovation design (Clark & Snow, [<reflink idref="bib23" id="ref99">23</reflink>]) to implement a targeted intervention at staggered time points across five campuses, allowing us to examine the outcomes associated with the intervention by analyzing data from treatment and control groups and within-institution departure from historical baselines. The staged innovation design is summarized in Table 1. In what follows, we provide a detailed overview of our intervention before providing information about our survey sample, measures, and analytical procedures.</p> <p>Table 1 Overview of staged innovation design</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="3"><p>Cohort A: Fall 2021</p><p>(<italic>n</italic> = 82)</p></th><th align="left" colspan="4"><p>Cohort B: Fall 2022</p><p>(<italic>n</italic> = 77)</p></th><th align="left" colspan="4"><p>Cohort C: Fall 2023</p><p>(<italic>n</italic> = 64)</p></th></tr></thead><tbody><tr><td align="left"><p>Campus 1 (<italic>n</italic> = 49)</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left"><p>Survey 2</p></td></tr><tr><td align="left"><p>Campus 2</p><p>(<italic>n</italic> = 93)</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left"><p>Survey 2</p></td></tr><tr><td align="left"><p>Campus 3</p><p>(<italic>n</italic> = 41)</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left"><p>Survey 2</p></td></tr><tr><td align="left"><p>Campus 4</p><p>(<italic>n</italic> = 13)</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left"><p>Survey 2</p></td></tr><tr><td align="left"><p>Campus 5</p><p>(<italic>n</italic> = 27)</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p>Control</p><p>→</p></td><td align="left" colspan="2"><p>Survey 2</p></td><td align="left"><p>Survey 1</p></td><td align="left"><p><bold>Intervention</bold></p><p>→</p></td><td align="left"><p>Survey 2</p></td></tr></tbody></table> </ephtml> </p> <p>Note. Table A2 shows a more detailed breakdown of the sample by campus and treatment/control condition</p> <hd id="AN0185703143-8">Intervention Procedures</hd> <p>Using SCCT as a guiding framework, our transfer student intervention targeted incoming vertical transfer students in CS majors across five participating research sites with a goal of fostering more positive PhD beliefs and interests. Influencing students' perceptions of value and expectations of success has been repeatedly demonstrated to bolster a wide range of desirable outcomes, from retention in STEM courses (e.g., Canning et al., [<reflink idref="bib18" id="ref100">18</reflink>]; Harackiewicz et al., [<reflink idref="bib37" id="ref101">37</reflink>]) to STEM achievement and motivation to pursue STEM (e.g., Brown et al., [<reflink idref="bib14" id="ref102">14</reflink>]; Walton et al., [<reflink idref="bib81" id="ref103">81</reflink>]) for students from groups that are minoritized within STEM courses and careers.</p> <p>Our intervention began with a thirty-minute session at transfer orientation, which included ten minutes dedicated to pre-test data collection. The short length was intentional, as it allows our intervention to be scalable and feasible to implement across campuses. While our study focuses on pathways to CS PhDs, we designed the orientation session to be relevant across disciplines, allowing universities to feasibly implement the intervention at university, college, or department-wide transfer orientation events. More specifically, the transfer orientation session provided students with accurate information on graduate training that conveys its feasibility for vertical transfer students and its prospective value for future careers and earnings (Posselt & Grodsky, [<reflink idref="bib67" id="ref104">67</reflink>]). We employed an audiovisual presentation that conveyed "did you know?" style messages related to the backgrounds of students who succeed in graduate education, the fiscal and career advantages of graduate degrees, and the existing supports and opportunities on campus that can help them in attaining successful admission to a PhD program in their field. These messages included three major findings: (<reflink idref="bib1" id="ref105">1</reflink>) students who attend community colleges are equally likely to succeed in earning a PhD as other students (Wang & Wickersham, [<reflink idref="bib84" id="ref106">84</reflink>]), (<reflink idref="bib2" id="ref107">2</reflink>) first-generation college students who pursue PhDs do not differ from continuing-generation students in their reported satisfaction with and sense of belonging to their degree programs (Roksa et al., [<reflink idref="bib69" id="ref108">69</reflink>]), and (<reflink idref="bib3" id="ref109">3</reflink>) students who have parenting responsibilities are equally likely to succeed as students who do not (Lott et al., [<reflink idref="bib52" id="ref110">52</reflink>]). Immediate and future financial outcomes were also discussed, such as the availability of tuition coverage and stipends via graduate assistantships. At the end of the session, we implemented a "saying-is-believing" intervention that asked participants to write a few sentences on the personal relevance and utility of PhD training and their likelihood of success if they choose to pursue a PhD (Harackiewicz & Priniski, [<reflink idref="bib37" id="ref111">37</reflink>]; Rosenzweig et al., [<reflink idref="bib71" id="ref112">71</reflink>]) to encourage students to see the value of pursuing a PhD.</p> <p>Immediately upon completion of the session, we distributed tailored brochures and workbooks to the vertical transfer CS students in attendance. These materials reiterated and expanded upon the presentation, focusing specifically on pathways to CS PhDs. Within one to seven days of the orientation session, we also shared our tailored materials with CS academic advisors and provided a one-hour workshop about how they could support transfer students who are interested in pursuing CS PhDs. This session reiterated the material shared during the student session and presented findings from broader literature on strategies to support vertical transfer students. Collectively, the orientation and advising materials were designed to correct myths about PhDs—emphasizing how PhD degrees are compatible with having a family, financially accessible through assistantships and fellowships, and successfully earned by a diverse group of students—to pique PhD interests among all vertical transfer students, particularly those from minoritized groups in CS.</p> <hd id="AN0185703143-9">Data Collection and Sample</hd> <p>This study draws on data collected across five research-intensive universities in California. Two surveys were administered to incoming cohorts of vertical transfer students in CS majors who entered their university in Fall 2021, 2022, and 2023. A pre-test survey was administered at the beginning of transfer orientation and received responses from 492 students (58% response rate). Of those who completed the first survey, 246 completed a post-test survey administered approximately four months later. This paper draws on data from the longitudinal sample of 246 students, including 168 students in the treatment group and 78 students in the control group. After removing 23 students from the sample who were inadvertently exposed to the treatment materials (i.e., brochures and workbooks) before the first survey was administered, the final analytic sample was <emph>N</emph> = 223.</p> <p>Among those in the analytic sample, 75% were men, 24% were women, and 1% indicated another non-binary gender identity. 43% were first-generation college students. Separately, 43% came from low-income/working-class backgrounds, 44% came from middle-class backgrounds, and 12% came from upper middle-class or wealthy backgrounds. In terms of the racial/ethnic breakdown of the sample, 33% were from East Asian groups, 25% were from South/Southeast Asian groups, 23% were white, 16% were Latine, 4% were Middle Eastern or Persian, 1% were Black, and 1% were from Indigenous groups.[<reflink idref="bib2" id="ref113">2</reflink>] Additional sample information is provided in Appendix A.</p> <hd id="AN0185703143-10">Measures</hd> <p>Table B1 provides information on all study measures. For the current analysis, we focused on three dependent variables capturing key PhD and graduate school-related beliefs and aspirations. Each of the dependent variables were measured on the pre-test and post-test survey. Guided by our conceptual framework, we first included a composite measure of PhD outcome expectations (i.e., perceptions that a PhD would lead to valuable outcomes), adapted from Borrego and colleagues ([<reflink idref="bib13" id="ref114">13</reflink>]). This variable was made up of four items (Cronbach's <emph>α</emph> = 0.74), each measured on a five-point agreement scale and averaged to create the composite measure. Specifically, the outcome expectations measure included the following items: (<reflink idref="bib1" id="ref115">1</reflink>) <emph>If I pursue a PhD</emph>,<emph> I could have a fulfilling career in my field</emph>; (<reflink idref="bib2" id="ref116">2</reflink>) <emph>If I pursue a PhD</emph>,<emph> I will be able to combine a professional career with having a balanced personal life</emph>; (<reflink idref="bib3" id="ref117">3</reflink>) <emph>Careers requiring a PhD would pay enough for me to support myself and my family</emph>; (<reflink idref="bib4" id="ref118">4</reflink>) <emph>Earning a PhD and having a family are compatible goals</emph>. To measure more tangible plans to earn a graduate degree, students were asked to report the highest degree they planned to attain from a list of options, and we dichotomized responses to capture graduate school plans (0 = No master's or PhD degree plans; 1 = Plans to earn a master's or PhD). To capture additional variance in PhD interests, students indicated their agreement on a five-point scale (1 = <emph>Strongly disagree</emph>; 5 = <emph>Strongly agree</emph>) with the following statement: <emph>I am interested in earning a PhD</emph>.</p> <p>The primary independent variable was a measure of whether or not students were exposed to the intervention (0 = control group; 1 = treatment group). Measures also elicited information on participant gender, race/ethnicity, first-generation college status, and socioeconomic status. Due to cell size constraints, non-binary students and women were aggregated to create a single dichotomous gender variable (0 = Men; 1 = Women and non-binary students) based on consistent findings that individuals not identifying as cisgender men are minoritized in computer science (Cech & Waidzunas, [<reflink idref="bib21" id="ref119">21</reflink>]; Skorodinsky, [<reflink idref="bib73" id="ref120">73</reflink>]). Also due to cell size constraints, to examine race/ethnicity, we first relied on dummy variables capturing the following four aggregated groups: (<reflink idref="bib1" id="ref121">1</reflink>) East Asian; (<reflink idref="bib2" id="ref122">2</reflink>) South/Southeast Asian; (<reflink idref="bib3" id="ref123">3</reflink>) White; (<reflink idref="bib4" id="ref124">4</reflink>) Black, Latine, Indigenous, and Middle Eastern/Persian students. Separately, we examined a single dichotomous race variable that categorized all students as either racially minoritized (i.e., Black, Latine, Indigenous, Middle Eastern/Persian, and/or South/Southeast Asian) or as the majority group (i.e., White and/or East Asian); students who selected at least one racially minoritized identity were coded as racially minoritized.[<reflink idref="bib3" id="ref125">3</reflink>] Examining dummy variables and a more aggregated, dichotomous variable to assess race/ethnicity allowed us to test for differential effects for specific racial/ethnic groups, while also assessing larger patterns for racially minoritized students that may otherwise have been missed due to power constraints.[<reflink idref="bib4" id="ref126">4</reflink>]</p> <hd id="AN0185703143-11">Analytical Procedures</hd> <p>To examine the first research question, we used OLS regression models to test the extent to which our intervention predicted PhD outcome expectations and interest in pursuing a PhD. We also used logistic regression to examine the relationship between the intervention and graduate school plans, accounting for the nested data structure by using robust clustered standard errors (described in the following paragraph). Next, we used interaction terms to assess how the outcomes associated with the intervention may differ by participants' race/ethnicity, gender, socioeconomic status, and first-generation college status. Simple slopes and graphical representations were used to examine the statistically significant interaction effects. For ease of interpretation, ordinal/continuous predictor variables were centered at 0 and dichotomous predictor variables were set to values of 0 and 1.</p> <p>Before building models, variables were examined to determine if cohort effects existed and to assess the nested data structure. Equality of variance across cohorts was assessed using Levene's test, and equality of means across cohorts was evaluated using a one-way ANOVA. Variances across cohorts differed only on the PhD interest scale, included on both the pre-test, <emph>F</emph>(<reflink idref="bib2" id="ref127">2</reflink>, 219) = 3.45, <emph>p</emph> =.03, and the post-test, <emph>F</emph>(<reflink idref="bib2" id="ref128">2</reflink>, 220) = 4.59, <emph>p</emph> =.01. Neither variances nor means for any other variables of interest were significantly different across cohorts (.58 ≤ <emph>p</emph> ≤.96). To determine the extent to which variation in scores could be attributed to students being nested within universities, intraclass correlation coefficients were calculated on a model where the second time point assessment was regressed on the baseline assessment, and both random intercepts and slopes were estimated. ICCs were small for PhD outcome expectations, graduate school plans, and PhD interest (ICCs ≤.04), suggesting that these correlations were very low. Given the lack of apparent cohort and university effects within our data, we moved forward with analyses that combined cohorts and universities. However, because the nested data structure was a critical component of our staged innovation design (in which we implemented our intervention over time across three cohorts and five universities), we accounted for the nested data structure in our regression analyses by using robust clustered standard errors.[<reflink idref="bib5" id="ref129">5</reflink>] These analyses were conducted in Mplus version 8.10 (Muthén & Muthén, [<reflink idref="bib57" id="ref130">57</reflink>]–[<reflink idref="bib57" id="ref131">57</reflink>]) using a sandwich estimator (i.e., Type = Complex in Mplus) to adjust for potential increases in Type I error rates due to participants being nested within universities and cohorts (Muthén & Satorra, [<reflink idref="bib58" id="ref132">58</reflink>]).</p> <hd id="AN0185703143-12">Results</hd> <p></p> <hd id="AN0185703143-13">Research Question One</hd> <p>The first research question asked about how exposure to the intervention might be associated with PhD beliefs, graduate school plans, and PhD interests. Table 2 summarizes the results from the corresponding regression analyses, including models with and without control variables capturing participants' gender, race/ethnicity, socioeconomic status, and first-generation college status. We found that exposure to the treatment positively predicted PhD outcome expectations, with and without the inclusion of demographic variables in the model (Table 2). Specifically, students exposed to the intervention had more positive beliefs about the value of earning a PhD, relative to those in the control group. However, the intervention did not significantly predict more tangible graduate school plans or interests in pursuing a PhD in the future. Though not directly related to our first research question, it should also be noted that, after accounting for all other variables, racially minoritized participants were significantly more likely to express interest in pursuing a PhD.</p> <p>Table 2 Main effects of treatment on phd outcome expectations, graduate school plans, and phd interests</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="3"><p>PhD Outcome Expectations</p></th><th align="left" colspan="4"><p>Graduate School Plans</p></th><th align="left" colspan="3"><p>PhD Interest</p></th></tr><tr><th align="left" /><th align="left"><p>B</p></th><th align="left"><p>SE</p></th><th align="left"><p><italic>p</italic></p></th><th align="left"><p>B</p></th><th align="left"><p>SE</p></th><th align="left"><p><italic>p</italic></p></th><th align="left"><p>OR</p></th><th align="left"><p>B</p></th><th align="left"><p>SE</p></th><th align="left"><p><italic>p</italic></p></th></tr></thead><tbody><tr><td align="left" /><td align="left" colspan="10"><p><italic>Treatment Model Results</italic></p></td></tr><tr><td align="left"><p>Intercept</p></td><td align="left"><p>3.35</p></td><td align="left"><p>0.07</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>1.08</p></td><td align="left"><p>0.29</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>2.94</p></td><td align="left"><p>2.70</p></td><td align="left"><p>0.06</p></td><td align="left"><p>< 0.001</p></td></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>0.47</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>1.58</p></td><td align="left"><p>0.12</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>4.85</p></td><td align="left"><p>0.58</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001***</p></td></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.013*</p></td><td align="left"><p>0.28</p></td><td align="left"><p>0.28</p></td><td align="left"><p>0.322</p></td><td align="left"><p>1.32</p></td><td align="left"><p>0.15</p></td><td align="left"><p>0.09</p></td><td align="left"><p>0.106</p></td></tr><tr><td align="left" /><td align="left" colspan="10"><p><italic>Demographic and Treatment Model Results</italic></p></td></tr><tr><td align="left"><p>Intercept</p></td><td align="left"><p>3.37</p></td><td align="left"><p>0.03</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>1.08</p></td><td align="left"><p>0.41</p></td><td align="left"><p>0.009</p></td><td align="left"><p>2.94</p></td><td align="left"><p>2.77</p></td><td align="left"><p>0.13</p></td><td align="left"><p>< 0.001</p></td></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>0.44</p></td><td align="left"><p>0.10</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>1.69</p></td><td align="left"><p>0.18</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>5.42</p></td><td align="left"><p>0.57</p></td><td align="left"><p>0.09</p></td><td align="left"><p>< 0.001***</p></td></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.010*</p></td><td align="left"><p>0.16</p></td><td align="left"><p>0.28</p></td><td align="left"><p>0.574</p></td><td align="left"><p>1.17</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.606</p></td></tr><tr><td align="left"><p>First-gen</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.192</p></td><td align="left"><p>0.25</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.213</p></td><td align="left"><p>1.28</p></td><td align="left"><p>-0.08</p></td><td align="left"><p>0.16</p></td><td align="left"><p>0.635</p></td></tr><tr><td align="left"><p>Gender (Women/Non-binary)</p></td><td align="left"><p>-0.04</p></td><td align="left"><p>0.07</p></td><td align="left"><p>0.580</p></td><td align="left"><p>-0.18</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.178</p></td><td align="left"><p>0.84</p></td><td align="left"><p>-0.12</p></td><td align="left"><p>0.09</p></td><td align="left"><p>0.157</p></td></tr><tr><td align="left"><p>SES</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.182</p></td><td align="left"><p>-0.15</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.484</p></td><td align="left"><p>0.86</p></td><td align="left"><p>-0.03</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.695</p></td></tr><tr><td align="left"><p>Racially Minoritized</p></td><td align="left"><p>-0.02</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.804</p></td><td align="left"><p>0.18</p></td><td align="left"><p>0.31</p></td><td align="left"><p>0.555</p></td><td align="left"><p>1.20</p></td><td align="left"><p>0.28</p></td><td align="left"><p>0.12</p></td><td align="left"><p>0.024*</p></td></tr></tbody></table> </ephtml> </p> <p>Note. This table includes findings from six different models (models for each dependent variable, with and without demographic variables). Pretest variables were measured using the same items as the dependent variables. The model predicting PhD outcome expectations explained approximately 25% of the variance without demographic variables (<emph>R</emph><sups><emph>2</emph> =</sups> 0.25) and 26% of the variance with demographic variables included (<emph>R</emph><sups><emph>2</emph> =</sups> 0.26). The model predicting PhD interests explained 32% of the variance without demographic variables (<emph>R</emph><sups><emph>2</emph> =</sups> 0.32) and 34% of the variance with demographic variables (<emph>R</emph><sups><emph>2</emph> =</sups> 0.34). Odds ratios (OR) are included only for models predicting graduate school plans, as this outcome was binary. Results are reported as unstandardized estimates. SES = socioeconomic status. ***<emph>p</emph> <.001; **<emph>p</emph> <.01; *<emph>p</emph> <.05</p> <hd id="AN0185703143-14">Research Question Two</hd> <p>The second research question concerned how the intervention might differentially predict the outcomes of interest as a product of students' demographic characteristics. To examine this question, we tested for two-way interaction effects between the treatment and each of the demographic variables of interest (Table 3). There were no significant interaction effects by gender or socioeconomic status, but two significant interactions suggested differential outcomes associated with the treatment on graduate school plans as a product of race/ethnicity and first-generation status. With regard to race/ethnicity, after finding no significant interactions between the intervention and individual dummy variables capturing racial/ethnic identities, we tested for interaction effects between racially minoritized students and students from white and East Asian groups in our sample. This revealed a significant interaction effect, suggesting that our intervention may have differentially predicted the likelihood of planning to attend graduate school for racially minoritized students. However, a closer look at simple slopes revealed that: (<reflink idref="bib1" id="ref133">1</reflink>) for racially minoritized students, the treatment positively but non-significantly predicted graduate school plans (simple slope <emph>B</emph> = 0.329, <emph>SE</emph> = 0.266, <emph>p</emph> =.216; <emph>OR</emph> = 1.390), and (<reflink idref="bib2" id="ref134">2</reflink>) for students from white and East Asian racial/ethnic groups, the treatment negatively and non-significantly predicted graduate school plans (simple slope <emph>B</emph> = − 0.347, <emph>SE</emph> = 0.214, <emph>p</emph> =.105; OR = 0.707). Additionally, while we found a significant interaction effect by first-generation status, tests of simple slopes showed that the effect was non-significant for subgroups. Specifically, our intervention was non-significantly and negatively associated with graduate school plans for first-generation students (simple slope <emph>B</emph> = − 0.365, <emph>SE</emph> = 0.275, <emph>p</emph> =.185; <emph>OR</emph> = 0.695) and non-significantly and positively associated with graduate school plans for students with college-educated parents (simple slope <emph>B</emph> = 0.310, <emph>SE</emph> = 0.217, <emph>p</emph> =.154; <emph>OR</emph> = 1.363). Overall, insights from the second research question suggest that our intervention may interact with race/ethnicity and first-generation college status. However, more research will be needed to parse interaction effects with subgroups using more diverse samples, as non-significant simple slopes and variable aggregation make these effects difficult to interpret within our sample.</p> <p>Table 3 Tests of interaction effects by race, gender, First-Gen status, and SES</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="3"><p>PhD Outcome Expectations</p></th><th align="left" colspan="4"><p>Graduate School Plans</p></th><th align="left" colspan="3"><p>PhD Interest</p></th></tr><tr><th align="left" /><th align="left"><p>B</p></th><th align="left"><p>SE</p></th><th align="left"><p><italic>p</italic></p></th><th align="left"><p>B</p></th><th align="left"><p>SE</p></th><th align="left"><p><italic>p</italic></p></th><th align="left"><p>OR</p></th><th align="left"><p>B</p></th><th align="left"><p>SE</p></th><th align="left"><p><italic>p</italic></p></th></tr></thead><tbody><tr><td align="left" /><td align="left" colspan="10"><p><italic>By Race</italic></p></td></tr><tr><td align="left"><p>Intercept</p></td><td align="left"><p>3.38</p></td><td align="left"><p>0.03</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>0.53</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.013</p></td><td align="left"><p>1.70</p></td><td align="left"><p>0.27</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.227</p></td></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>0.46</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>1.27</p></td><td align="left"><p>1.52</p></td><td align="left"><p>0.404</p></td><td align="left"><p>3.55</p></td><td align="left"><p>0.04</p></td><td align="left"><p>0.26</p></td><td align="left"><p>0.892</p></td></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>0.18</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.002**</p></td><td align="left"><p>-0.35</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.105</p></td><td align="left"><p>0.71</p></td><td align="left"><p>0.27</p></td><td align="left"><p>0.12</p></td><td align="left"><p>0.024*</p></td></tr><tr><td align="left"><p>Racially Minoritized</p></td><td align="left"><p>-0.07</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.520</p></td><td align="left"><p>-0.12</p></td><td align="left"><p>0.16</p></td><td align="left"><p>0.458</p></td><td align="left"><p>0.89</p></td><td align="left"><p>0.60</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.004*</p></td></tr><tr><td align="left"><p>Treatment x Racially Minoritized</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.424</p></td><td align="left"><p>0.68</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.002**</p></td><td align="left"><p>1.97</p></td><td align="left"><p>-0.43</p></td><td align="left"><p>0.24</p></td><td align="left"><p>0.077</p></td></tr><tr><td align="left" /><td align="left" colspan="10"><p><italic>By Gender</italic></p></td></tr><tr><td align="left"><p>Intercept</p></td><td align="left"><p>3.31</p></td><td align="left"><p>0.07</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>0.49</p></td><td align="left"><p>2.53</p></td><td align="left"><p>0.990</p></td><td align="left"><p>1.64</p></td><td align="left"><p>2.52</p></td><td align="left"><p>0.16</p></td><td align="left"><p>< 0.001</p></td></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>0.44</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>-0.06</p></td><td align="left"><p>89.33</p></td><td align="left"><p>0.999</p></td><td align="left"><p>0.94</p></td><td align="left"><p>0.58</p></td><td align="left"><p>0.07</p></td><td align="left"><p>< 0.001***</p></td></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.115</p></td><td align="left"><p>0.09</p></td><td align="left"><p>0.38</p></td><td align="left"><p>0.798</p></td><td align="left"><p>1.09</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.686</p></td></tr><tr><td align="left"><p>Gender (Women/Non-binary)</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.587</p></td><td align="left"><p>0.18</p></td><td align="left"><p>0.16</p></td><td align="left"><p>0.250</p></td><td align="left"><p>1.20</p></td><td align="left"><p>0.27</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.227</p></td></tr><tr><td align="left"><p>Treatment x Gender</p></td><td align="left"><p>0.06</p></td><td align="left"><p>0.15</p></td><td align="left"><p>0.669</p></td><td align="left"><p>-0.07</p></td><td align="left"><p>0.31</p></td><td align="left"><p>0.813</p></td><td align="left"><p>0.93</p></td><td align="left"><p>0.04</p></td><td align="left"><p>0.26</p></td><td align="left"><p>0.892</p></td></tr><tr><td align="left" /><td align="left" colspan="10"><p><italic>By First-Gen</italic></p></td></tr><tr><td align="left"><p>Intercept</p></td><td align="left"><p>3.35</p></td><td align="left"><p>0.04</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>0.53</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.016</p></td><td align="left"><p>1.70</p></td><td align="left"><p>2.63</p></td><td align="left"><p>0.09</p></td><td align="left"><p>< 0.001</p></td></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>0.45</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>1.27</p></td><td align="left"><p>1.65</p></td><td align="left"><p>0.444</p></td><td align="left"><p>3.55</p></td><td align="left"><p>0.55</p></td><td align="left"><p>0.09</p></td><td align="left"><p>< 0.001***</p></td></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>0.19</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.022*</p></td><td align="left"><p>0.31</p></td><td align="left"><p>0.22</p></td><td align="left"><p>0.154</p></td><td align="left"><p>1.36</p></td><td align="left"><p>0.31</p></td><td align="left"><p>0.14</p></td><td align="left"><p>0.027*</p></td></tr><tr><td align="left"><p>First-gen</p></td><td align="left"><p>-0.02</p></td><td align="left"><p>0.17</p></td><td align="left"><p>0.929</p></td><td align="left"><p>0.43</p></td><td align="left"><p>0.17</p></td><td align="left"><p>0.014*</p></td><td align="left"><p>1.53</p></td><td align="left"><p>0.26</p></td><td align="left"><p>0.27</p></td><td align="left"><p>0.328</p></td></tr><tr><td align="left"><p>Treatment x First-gen</p></td><td align="left"><p>0.11</p></td><td align="left"><p>0.20</p></td><td align="left"><p>0.570</p></td><td align="left"><p>-0.67</p></td><td align="left"><p>0.27</p></td><td align="left"><p>0.014*</p></td><td align="left"><p>0.51</p></td><td align="left"><p>-0.46</p></td><td align="left"><p>0.29</p></td><td align="left"><p>0.116</p></td></tr><tr><td align="left" /><td align="left" colspan="10"><p><italic>By SES</italic></p></td></tr><tr><td align="left"><p>Intercept</p></td><td align="left"><p>3.36</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>0.50</p></td><td align="left"><p>0.06</p></td><td align="left"><p>< 0.001</p></td><td align="left"><p>1.17</p></td><td align="left"><p>2.68</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001</p></td></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>0.46</p></td><td align="left"><p>0.10</p></td><td align="left"><p>< 0.001***</p></td><td align="left"><p>0.00</p></td><td align="left"><p>11.52</p></td><td align="left"><p>0.999</p></td><td align="left"><p>1.00</p></td><td align="left"><p>0.60</p></td><td align="left"><p>0.08</p></td><td align="left"><p>< 0.001***</p></td></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>0.23</p></td><td align="left"><p>0.10</p></td><td align="left"><p>0.020*</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.16</p></td><td align="left"><p>0.417</p></td><td align="left"><p>1.14</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.12</p></td><td align="left"><p>0.280</p></td></tr><tr><td align="left"><p>SES</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.08</p></td><td align="left"><p>0.110</p></td><td align="left"><p>-0.41</p></td><td align="left"><p>0.21</p></td><td align="left"><p>0.054</p></td><td align="left"><p>0.66</p></td><td align="left"><p>-0.01</p></td><td align="left"><p>0.14</p></td><td align="left"><p>0.960</p></td></tr><tr><td align="left"><p>Treatment x SES</p></td><td align="left"><p>-0.08</p></td><td align="left"><p>0.13</p></td><td align="left"><p>0.549</p></td><td align="left"><p>0.47</p></td><td align="left"><p>0.26</p></td><td align="left"><p>0.071</p></td><td align="left"><p>1.61</p></td><td align="left"><p>0.00</p></td><td align="left"><p>0.15</p></td><td align="left"><p>0.978</p></td></tr></tbody></table> </ephtml> </p> <p>Note. Pretest variables were measured using the same items as the dependent variables. Results are reported as unstandardized estimates. SES = socioeconomic status. ***<emph>p</emph> <.001; **<emph>p</emph> <.01; *<emph>p</emph> <.05</p> <hd id="AN0185703143-15">Limitations</hd> <p>While our staged innovation design positions us to make limited causal inferences by accounting for potential effects of university and cohort, students were not randomly assigned to the treatment and control groups. To mitigate this limitation, we controlled for incoming pretest scores in regressions predicting our dependent variables and have included information about pretest and dependent variable scores by university and cohort in Appendix C. Additionally, our focus on vertical transfer students in CS majors across five research-intensive universities in the state of California means that results may not generalize to other disciplinary, institutional, or state contexts. Further, because of the lack of gender and racial/ethnic diversity in CS as a whole, cell sizes limited our ability to disaggregate all social identity groups or examine intersecting identity groups. Through necessary aggregation into dichotomous categorical values, it is possible that we limited our ability to detect meaningful effects. Finally, we focused on vertical transfer students' first year at their receiving universities. More research will be needed to determine if similar interventions impact degree trajectories over longer periods of time.</p> <hd id="AN0185703143-16">Discussion and Implications</hd> <p>Building pathways from community colleges to PhD study is critical for diversifying the professoriate in CS (Blaney & Wofford, [<reflink idref="bib6" id="ref135">6</reflink>]), and this study expands what is currently known about educational trajectories from community colleges to graduate education. Our results suggest that brief interventions targeting expectancy beliefs may significantly increase vertical transfer students' perceived value of pursuing PhDs (i.e., PhD outcome expectations). This is an especially important finding, given prior research on how such beliefs about graduate school (i.e., outcome expectations) positively predict more tangible plans to enroll in a graduate program (Borrego et al., [<reflink idref="bib13" id="ref136">13</reflink>]). Other results suggest that such interventions may interact with students' identities and backgrounds to shape plans to attend graduate school, though more research will be needed to understand how, if at all, similar interventions may uniquely impact students from minoritized groups in STEM.</p> <hd id="AN0185703143-17">Understanding the Efficacy of Brief Psychological and Informational Interventions</hd> <p>As part of interpreting our results, it is important to highlight that our intervention was limited to a 30-minute orientation session and follow-up advising materials. Yet, this brief intervention still significantly predicted PhD outcome expectations four months later. Recognizing that transfer students face myriad competing demands on their time that occupy their focus during their first year at receiving universities (Blaney, Feldon et al., [<reflink idref="bib9" id="ref137">9</reflink>]), it is remarkable that such a brief intervention could have a lasting effect on students' beliefs about graduate training. It may be that faculty and others who engage undergraduates in STEM endeavors speak to transfer students about PhD training opportunities so rarely that even fleeting targeted messages can significantly predict changes in transfer students' perspectives. However, more research is needed to understand how and why our intervention may have been effective.</p> <p>Our results can be further understood through prior research and theory on the efficacy of psychological and informational interventions. Despite some studies showing the efficacy of brief psychological interventions (see Harackiewicz & Priniski, [<reflink idref="bib37" id="ref138">37</reflink>]), other recent studies have documented null effects from similarly brief informational initiatives (Oreopoulos & Petronijevic, [<reflink idref="bib64" id="ref139">64</reflink>]). Perhaps the reason why our intervention did positively predict transfer students' beliefs about PhDs is because it combined elements from saying-is-believing <emph>and</emph> informational interventions, seeking to foster positive beliefs about graduate school while also providing information about next steps students can take to prepare for graduate training. Within future research, experimental studies may be useful for determining the specific elements of interventions that encourage students' graduate degree pursuits. Similarly, future studies can use mixed methods to better understand how students make meaning of intervention activities.</p> <p>It is also important to note that the scope of significant effects from our intervention is limited to predicting changes in PhD beliefs. Our intervention did not significantly predict more tangible interests and/or plans for pursuing graduate school. Taken together, our findings underscore and extend prior research demonstrating the potential efficacy of brief interventions (e.g., Harackiewicz & Priniski, [<reflink idref="bib37" id="ref140">37</reflink>]; Rosenzweig et al., [<reflink idref="bib71" id="ref141">71</reflink>]) but also highlight the need for additional research on strategies to support transfer students' more tangible graduate school interests, plans, and preparation at their receiving universities.</p> <hd id="AN0185703143-18">Building Sustained University Supports</hd> <p>Our results raise questions about the possibility of employing more sustained efforts, including those that intervene to foster more positive faculty and other academic interactions in support of transfer students' access to graduate school. Indeed, SCCT holds that learning experiences over time are a major influence on both career and degree outcome expectations. For instance, engagement with faculty and peers within learning environments can moderate the role of outcome expectations and self-efficacy on goal selection and actions taken to pursue those goals (Lent et al., [<reflink idref="bib49" id="ref142">49</reflink>], [<reflink idref="bib46" id="ref143">46</reflink>], [<reflink idref="bib50" id="ref144">50</reflink>]). Within the current sample, other analyses show that receiving PhD information specifically from university faculty members positively predicts PhD interests among vertical transfer students in CS (Blaney, Feldon et al., [<reflink idref="bib9" id="ref145">9</reflink>]). In light of these findings, we conclude that our intervention may have been effective at improving transfer students' beliefs about PhDs but that more sustained support structures that engage university faculty will likely be necessary to increase transfer students' pathways toward PhD study. Future research examining prospective strategies for boosting interest in PhDs among vertical transfer students should explore how the impacts of discrete interventions interact with the sustained experiences of students as they navigate their academic programs and co-curricular experiences.</p> <hd id="AN0185703143-19">Considering Inequities and Variation among Transfer Students</hd> <p>Informed by the present results, future research should consider how strategies to support PhD pathways may differentially predict graduate school outcomes among transfer students. Prior applications of SCCT indicate that supportive learning environments and access to resources can vary by students' social identities (e.g., gender, social class; Flores et al., [<reflink idref="bib33" id="ref146">33</reflink>]; Williams & Subich, [<reflink idref="bib86" id="ref147">86</reflink>]). Within CS specifically, research reveals how access to meaningful faculty interactions and supportive CS environments may not be equally available and/or beneficial to students holding minoritized identities (Blaney, Feldon et al., [<reflink idref="bib9" id="ref148">9</reflink>]; Gehringer et al., [<reflink idref="bib35" id="ref149">35</reflink>]; Lehman et al., [<reflink idref="bib44" id="ref150">44</reflink>]; Varma, [<reflink idref="bib80" id="ref151">80</reflink>]). Thus, it will be important for researchers and practitioners to consider if interventions can mitigate inequities by making graduate school information readily available, while also exploring strategies to address underlying inequities in student experiences and faculty interactions. In the case of our study, while we detected some significant conditional effects of our intervention by race/ethnicity and first-generation college status, non-significant simple slopes and power constraints limited our ability to interpret these effects.</p> <hd id="AN0185703143-20">Synthesis of Implications for Practice</hd> <p>Consideration of vertical transfer students as an important population to recruit to (and later retain in) CS doctoral education is a relatively new direction for increasing and diversifying the composition of future CS faculty. While no one strategy is likely to achieve the comprehensive goals of expanding and diversifying the pool of prospective future faculty in computer science, the combination of multiple approaches that leverage SCCT mechanisms may yield outsized benefits in actualizing the potential of transfer students to earn PhDs in CS.</p> <p>Our results highlight the potential of targeted, theoretically guided interventions to help vertical transfer students recognize the value of pursuing a PhD. Guided by our findings, university faculty and staff can collaborate to ensure that information about the value and utility of PhD training is widely available to vertical transfer students through encouraging informational sessions, while faculty concurrently work to equitably provide ongoing, personalized exposure to graduate training encouragement and preparation (e.g., research experiences).</p> <hd id="AN0185703143-21">Conclusions</hd> <p>While vertical transfer students represent a diverse and high achieving group to recruit to PhD programs in STEM, they may face obstacles in their degree pursuits which can limit their interests in pursuing PhDs. Our findings suggest that brief interventions may be effective at improving vertical transfer students' beliefs about the value of PhD study in CS. More research is needed to explore strategies for translating positive PhD beliefs to increased interest in <emph>actually pursuing</emph> these pathways. In the meantime, we argue that discrete interventions should be paired with larger efforts to develop sustained support structures for transfer students. Future research, theory, and practice should consider other strategies to build pathways from community colleges to graduate training in CS and other fields of study, recognizing the potential these pathways hold to diversify graduate programs.</p> <hd id="AN0185703143-22">Appendix A. Overview of Sample</hd> <p>Table A1 Composition of sample</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left" colspan="3"><p>Percent Among</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>All</p></td><td align="left"><p>Control</p></td><td align="left"><p>Treatment</p></td></tr><tr><td align="left"><p>Gender</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Men</p></td><td align="left"><p>74.7</p></td><td align="left"><p>67.9</p></td><td align="left"><p>78.4</p></td></tr><tr><td align="left"><p> Women</p></td><td align="left"><p>24.0</p></td><td align="left"><p>32.1</p></td><td align="left"><p>19.4</p></td></tr><tr><td align="left"><p> Non-binary</p></td><td align="left"><p>1.4</p></td><td align="left"><p>0.0</p></td><td align="left"><p>2.2</p></td></tr><tr><td align="left"><p>First-generation</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Yes</p></td><td align="left"><p>42.7</p></td><td align="left"><p>32.9</p></td><td align="left"><p>48.1</p></td></tr><tr><td align="left"><p> No</p></td><td align="left"><p>57.3</p></td><td align="left"><p>67.1</p></td><td align="left"><p>51.9</p></td></tr><tr><td align="left"><p>SES</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Low-income/Working class</p></td><td align="left"><p>43.3</p></td><td align="left"><p>37.0</p></td><td align="left"><p>46.9</p></td></tr><tr><td align="left"><p> Middle class</p></td><td align="left"><p>44.3</p></td><td align="left"><p>45.2</p></td><td align="left"><p>43.8</p></td></tr><tr><td align="left"><p> Wealthy or upper-middle class</p></td><td align="left"><p>12.4</p></td><td align="left"><p>17.8</p></td><td align="left"><p>9.4</p></td></tr><tr><td align="left"><p>Race/Ethnicity</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> East Asian</p></td><td align="left"><p>35.6</p></td><td align="left"><p>42.3</p></td><td align="left"><p>31.5</p></td></tr><tr><td align="left"><p> South/Southeast Asian</p></td><td align="left"><p>27.4</p></td><td align="left"><p>19.2</p></td><td align="left"><p>32.3</p></td></tr><tr><td align="left"><p> White</p></td><td align="left"><p>24.0</p></td><td align="left"><p>20.5</p></td><td align="left"><p>26.2</p></td></tr><tr><td align="left"><p> Latine</p></td><td align="left"><p>16.7</p></td><td align="left"><p>11.5</p></td><td align="left"><p>19.8</p></td></tr><tr><td align="left"><p> Middle Eastern or Persian</p></td><td align="left"><p>4.3</p></td><td align="left"><p>3.8</p></td><td align="left"><p>4.6</p></td></tr><tr><td align="left"><p> Black</p></td><td align="left"><p>1.4</p></td><td align="left"><p>1.3</p></td><td align="left"><p>1.5</p></td></tr><tr><td align="left"><p> Indigenous</p></td><td align="left"><p>1.0</p></td><td align="left"><p>0.0</p></td><td align="left"><p>1.5</p></td></tr></tbody></table> </ephtml> </p> <p>Note. Percentages add to more than 100%, as students were able to select more than one racial/ethnic identity. Demographics are treated as covariates in regression models</p> <p>Table A2 Distribution of Sample by University and treatment/control</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>University</p></th><th align="left" colspan="3"><p>Number of Participants</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>All</p></td><td align="left"><p>Control</p></td><td align="left"><p>Treatment</p></td></tr><tr><td align="left"><p>Campus 1</p></td><td align="left"><p>49</p></td><td align="left"><p>0</p></td><td align="left"><p>49</p></td></tr><tr><td align="left"><p>Campus 2</p></td><td align="left"><p>93</p></td><td align="left"><p>24</p></td><td align="left"><p>69</p></td></tr><tr><td align="left"><p>Campus 3</p></td><td align="left"><p>41</p></td><td align="left"><p>34</p></td><td align="left"><p>7</p></td></tr><tr><td align="left"><p>Campus 4</p></td><td align="left"><p>13</p></td><td align="left"><p>8</p></td><td align="left"><p>5</p></td></tr><tr><td align="left"><p>Campus 5</p></td><td align="left"><p>27</p></td><td align="left"><p>12</p></td><td align="left"><p>15</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0185703143-23">Appendix B. Measurement Information</hd> <p>Table B1 Overview of survey measures</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p>Variable Description</p></th><th align="left"><p>Range or coding scheme</p></th></tr></thead><tbody><tr><td align="left" colspan="2"><p><italic><underline>Primary Independent Variable</underline></italic></p></td><td align="left" /></tr><tr><td align="left"><p>Treatment</p></td><td align="left"><p>Dichotomous measure capturing exposure to the intervention</p></td><td align="left"><p>0 = Control group; 1 = Treatment group</p></td></tr><tr><td align="left"><p><italic><underline>Identity Variables</underline></italic></p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Gender</p></td><td align="left"><p>Please indicate your gender by selecting one of the following: Man, Woman, Another Gender Identity</p></td><td align="left"><p>0 = Men; 1 = Women or Non-binary identity</p></td></tr><tr><td align="left"><p>Race/ethnicity</p></td><td align="left"><p>Please indicate your race/ethnicity by selecting all that apply: Asian/Asian American; Black; Latina/o/x; Middle Eastern or Persian; Native American or Alaska Native; Native Hawaiian/Pacific; White</p></td><td align="left"><p>0 = White or East Asian; 1 = Racially Minoritized</p></td></tr><tr><td align="left"><p>First-generation college status</p></td><td align="left"><p>Did one or more of your parents or guardians earn a bachelor's degree?</p></td><td align="left"><p>0 = No; 1 = Yes</p></td></tr><tr><td align="left"><p>Socioeconomic status</p></td><td align="left"><p>Which of the following best describes your social class when you were growing up?</p></td><td align="left"><p>1 = Low-income or working class; 2 = Middle class; 3 = Wealthy or upper-middle class</p></td></tr><tr><td align="left" colspan="2"><p><italic><underline>Dependent Variables/Pretests</underline></italic></p></td><td align="left" /></tr><tr><td align="left"><p>PhD outcome expectations</p></td><td align="left"><p>Four-item composite variable adapted from Borrego et al. (<xref ref-type="bibr" rid="bibr13">2018</xref>)</p></td><td align="left"><p>1 (low outcome expectations) to 5 (high outcome expectations)</p></td></tr><tr><td align="left"><p>Graduate school plans</p></td><td align="left"><p>What is the highest degree you plan to attain in your lifetime?</p></td><td align="left"><p>0 = Bachelors; 1 = Masters or PhD</p></td></tr><tr><td align="left"><p>PhD interest</p></td><td align="left"><p>Please indicate your agreement with the following statements: I am interested in earning a PhD</p></td><td align="left"><p>1 = Strongly disagree; 2 = Disagree; 3 = Neither agree nor disagree; 4 = Agree; 5 = Strongly agree</p></td></tr></tbody></table> </ephtml> </p> <p>Note. Students who selected an Asian/Asian American identity were asked to report their specific racial/ethnic identities as East Asian, Southeast Asian, or South Asian. South and Southeast Asian students were included as racially minoritized. Preliminary analyses also examined more disaggregated racial/ethnic groups using dummy variables, as described in the measures section of the manuscript</p> <hd id="AN0185703143-24">Appendix C. Information on Nested Data Structure: Variation by Campus and Cohort</hd> <p>Table C1 PhD outcome expectations and interests by university and treatment/control</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="5"><p>Mean (SD)</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>Campus 1</p></td><td align="left"><p>Campus 2</p></td><td align="left"><p>Campus 3</p></td><td align="left"><p>Campus 4</p></td><td align="left"><p>Campus 5</p></td></tr><tr><td align="left"><p><italic>Control Group</italic></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PhD Outcome Expectations</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>N/A</p></td><td align="left"><p>3.24</p><p>(0.73)</p></td><td align="left"><p>3.70</p><p>(0.60)</p></td><td align="left"><p>3.11</p><p>(0.61)</p></td><td align="left"><p>3.52</p><p>(0.43)</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>N/A</p></td><td align="left"><p>3.15</p><p>(0.77)</p></td><td align="left"><p>3.46</p><p>(0.58)</p></td><td align="left"><p>3.06</p><p>(0.58)</p></td><td align="left"><p>3.45</p><p>(0.33)</p></td></tr><tr><td align="left"><p>PhD Interests</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>N/A</p></td><td align="left"><p>2.75</p><p>(1.03)</p></td><td align="left"><p>3.21</p><p>(1.41)</p></td><td align="left"><p>2.13</p><p>(0.64)</p></td><td align="left"><p>3.33</p><p>(1.07)</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>N/A</p></td><td align="left"><p>2.50</p><p>(1.18)</p></td><td align="left"><p>2.97</p><p>(1.14)</p></td><td align="left"><p>2.38</p><p>(1.06)</p></td><td align="left"><p>2.83</p><p>(1.11)</p></td></tr><tr><td align="left"><p><italic>Treatment Group</italic></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PhD Outcome Expectations</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>3.54</p><p>(0.71)</p></td><td align="left"><p>3.51</p><p>(0.67)</p></td><td align="left"><p>3.32</p><p>(0.51)</p></td><td align="left"><p>3.95</p><p>(0.80)</p></td><td align="left"><p>3.82</p><p>(0.72)</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>3.57</p><p>(0.69)</p></td><td align="left"><p>3.50</p><p>(0.69)</p></td><td align="left"><p>3.46</p><p>(0.55)</p></td><td align="left"><p>3.75</p><p>(1.03)</p></td><td align="left"><p>3.88</p><p>(0.52)</p></td></tr><tr><td align="left"><p>PhD Interests</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>2.69</p><p>(1.12)</p></td><td align="left"><p>3.01</p><p>(0.98)</p></td><td align="left"><p>2.43</p><p>(0.79)</p></td><td align="left"><p>2.80</p><p>(1.30)</p></td><td align="left"><p>3.20</p><p>(0.94)</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>2.67</p><p>(1.21)</p></td><td align="left"><p>2.87</p><p>(1.06)</p></td><td align="left"><p>2.29</p><p>(0.76)</p></td><td align="left"><p>3.00</p><p>(1.58)</p></td><td align="left"><p>3.33</p><p>(1.18)</p></td></tr></tbody></table> </ephtml> </p> <p>Table C2 Percent of sample with grad school plans by university and treatment/control</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="5"><p>Percent Among</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>Campus 1</p></td><td align="left"><p>Campus 2</p></td><td align="left"><p>Campus 3</p></td><td align="left"><p>Campus 4</p></td><td align="left"><p>Campus 5</p></td></tr><tr><td align="left"><p><italic>Control</italic></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>N/A</p></td><td align="left"><p>54.17</p></td><td align="left"><p>60.61</p></td><td align="left"><p>25.00</p></td><td align="left"><p>58.33</p></td></tr><tr><td align="left"><p>DV</p></td><td align="left"><p>N/A</p></td><td align="left"><p>29.17</p></td><td align="left"><p>58.82</p></td><td align="left"><p>12.50</p></td><td align="left"><p>58.33</p></td></tr><tr><td align="left"><p><italic>Treatment</italic></p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>36.73</p></td><td align="left"><p>50.72</p></td><td align="left"><p>28.57</p></td><td align="left"><p>20.00</p></td><td align="left"><p>53.33</p></td></tr><tr><td align="left"><p>DV</p></td><td align="left"><p>42.86</p></td><td align="left"><p>50.72</p></td><td align="left"><p>28.57</p></td><td align="left"><p>20.00</p></td><td align="left"><p>53.33</p></td></tr></tbody></table> </ephtml> </p> <p>Table C3 PhD outcome expectations and interests by cohort and treatment/control</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="3"><p>Mean (SD)</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>Cohort 1</p></td><td align="left"><p>Cohort 2</p></td><td align="left"><p>Cohort 3</p></td></tr><tr><td align="left"><p><italic>Control Group</italic></p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PhD Outcome Expectations</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>3.50</p><p>(0.65)</p></td><td align="left"><p>3.42</p><p>(0.65)</p></td><td align="left"><p>N/A</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>3.30</p><p>(0.63)</p></td><td align="left"><p>3.37</p><p>(0.65)</p></td><td align="left"><p>N/A</p></td></tr><tr><td align="left"><p>PhD Interests</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>2.92</p><p>(1.28)</p></td><td align="left"><p>3.08</p><p>(1.12)</p></td><td align="left"><p>N/A</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>2.62</p><p>(1.19)</p></td><td align="left"><p>3.00</p><p>(1.02)</p></td><td align="left"><p>N/A</p></td></tr><tr><td align="left"><p><italic>Treatment Group</italic></p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>PhD Outcome Expectations</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>3.67</p><p>(0.61)</p></td><td align="left"><p>3.44</p><p>(0.70)</p></td><td align="left"><p>3.60</p><p>(0.70)</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>3.53</p><p>(0.71)</p></td><td align="left"><p>3.58</p><p>(0.59)</p></td><td align="left"><p>3.59</p><p>(0.74)</p></td></tr><tr><td align="left"><p>PhD Interests</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p> Pretest</p></td><td align="left"><p>2.67</p><p>(1.15)</p></td><td align="left"><p>2.96</p><p>(1.00)</p></td><td align="left"><p>2.94</p><p>(1.01)</p></td></tr><tr><td align="left"><p> DV</p></td><td align="left"><p>2.50</p><p>(1.28)</p></td><td align="left"><p>2.86</p><p>(0.98)</p></td><td align="left"><p>2.95</p><p>(1.17)</p></td></tr></tbody></table> </ephtml> </p> <p>Table C4 Percent of sample with grad school plans by cohort and treatment/control</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left" colspan="3"><p>Percent Among</p></th></tr></thead><tbody><tr><td align="left" /><td align="left"><p>Cohort 1</p></td><td align="left"><p>Cohort 2</p></td><td align="left"><p>Cohort 3</p></td></tr><tr><td align="left"><p><italic>Control</italic></p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>50.0</p></td><td align="left"><p>64.0</p></td><td align="left"><p>N/A</p></td></tr><tr><td align="left"><p>DV</p></td><td align="left"><p>42.3</p></td><td align="left"><p>50.0</p></td><td align="left"><p>N/A</p></td></tr><tr><td align="left"><p><italic>Treatment</italic></p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>Pretest</p></td><td align="left"><p>26.7</p></td><td align="left"><p>54.9</p></td><td align="left"><p>43.8</p></td></tr><tr><td align="left"><p>DV</p></td><td align="left"><p>43.3</p></td><td align="left"><p>45.1</p></td><td align="left"><p>48.4</p></td></tr></tbody></table> </ephtml> </p> <hd id="AN0185703143-25">Funding</hd> <p>This manuscript is based upon work supported by the National Science Foundation (NSF 2439166). 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In particular, scholarship has explored how Black, Indigenous, Middle Eastern/Persian, and South/Southeast Asian are racially and ethnically minoritized within CS and other higher education spaces (Lehman et al., [44]; Mesouani, [55]; Vakil & McKinney de Royston, [79]).</bibtext> </blist> <blist> <bibtext> Individual dummy variables were not significant in the regression models. As such, the findings that follow focus on the dichotomous variable capturing racially minoritized status.</bibtext> </blist> <blist> <bibtext> Because of the limited number of cohorts and universities, we created a single variable that accounted for the nesting by cohort and campus, in which all participants were assigned a number ranging from one to 15, based on their cohort and campus (i.e., students in the first cohort were assigned a number from one to five, students in cohort two were assigned a number from six to 10, and cohort three students were assigned a number ranging from 11 to 15 based on the university they attended). 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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 24
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation (NSF)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: 2439166
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Two+Year+Colleges%22">Two Year Colleges</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22College+Transfer+Students%22">College Transfer Students</searchLink><br /><searchLink fieldCode="DE" term="%22Community+College+Students%22">Community College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+Study%22">Graduate Study</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Education%22">Access to Education</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Interests%22">Student Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11162-025-09843-9
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0361-0365<br />1573-188X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Community college transfer students represent a diverse and talented group to recruit to PhD and other graduate programs. Yet, little is known about practical strategies to support community college transfer students' access to graduate training. Focusing specifically on transfer students in computer science and guided by social cognitive career theory, this manuscript draws on survey data from over 200 community college transfer students and utilizes a staged innovation design to examine a new intervention designed to pique transfer students' interests in PhD study. Findings suggest that brief targeted interventions can significantly predict transfer students' perceptions about PhD study, but that more sustained efforts will likely be necessary to influence transfer students' more tangible degree plans. In addition to highlighting implications for future research, we identify strategies for faculty and staff seeking to support community college transfer students and build access to graduate training.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1471269
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1471269
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11162-025-09843-9
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
    Subjects:
      – SubjectFull: College Transfer Students
        Type: general
      – SubjectFull: Community College Students
        Type: general
      – SubjectFull: Graduate Study
        Type: general
      – SubjectFull: Access to Education
        Type: general
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Student Interests
        Type: general
      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
    Titles:
      – TitleFull: Building Transfer Student Interest in Computer Science PhDs: Examining an Advising Intervention Using a Staged Innovation Design
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jennifer M. Blaney
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          Name:
            NameFull: David F. Feldon
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            NameFull: Annie M. Wofford
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            NameFull: Kaylee Litson
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          Dates:
            – D: 01
              M: 06
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0361-0365
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              Value: 66
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Research in Higher Education
              Type: main
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