Psychosocial Readiness for College: A Multidimensional Model and Measure for Students Entering College in Their Twenties

Abstract

Dropping out of college is often linked to insufficient academic or psychosocial readiness. Therefore, assessing students’ readiness, preferably before they begin their first year of college, may help identify those at risk of dropping out. The present study aimed to develop and test a multidimensional measure that assesses psychosocial factors promoting student persistence. The Psychosocial Readiness for College questionnaire (PRCq) aims to assess the readiness of students who have deferred entering college to their twenties and comprises six dimensions: Academic Self-Efficacy, Educational Commitment, Social Comfort, Campus Engagement, Self-Discipline, and Resilience. The PRCq was administered to five cohorts of first-year college students in Israel (N = 7,382). The PRCq demonstrated good psychometric properties, and exploratory and confirmatory factor analyses supported its six dimensions. The PRCq demonstrated measurement invariance across age, gender, SES, first vs. non-first-generation students, and institution type. The PRCq’s construct validity was supported by the negligible associations between the scale scores and high-school GPA and pre-college scholastic aptitude test, and the pattern of associations with the Big-5 personality factors. The PRCq’s predictive validity was supported by the student dropout rate after one year. Implications for students at risk of dropping out and interventions to decrease this risk are discussed.

Keywords

psychosocial readiness for college first year of college academic performance retention college dropout

Introduction

Attaining a college degree facilitates the graduates’ integration into the world of work (Grebennikov & Shah, 2012; Van Rhijn et al., 2016). College graduates are more likely than non-graduates to achieve higher rates of employment and income and, consequently, make higher tax payments to the state (Ma et al., 2016). College retention rates have attracted the attention of researchers and policymakers (e.g., Ajayi et al., 2021; Krumrei-Mancuso et al., 2013), noting that specific groups have demonstrated relatively lower retention rates. These groups include men (Conger & Dickson, 2017), students of low socioeconomic status (SES), first-generation college students (FGCS; King & Ndum, 2017), and older students (Gilardi & Guglielmetti, 2011). As trends prolonging the high school-to-college transition are growing (Markle, 2015), particular attention to older-age college students is warranted. Thus, the present research aimed to assess psychosocial readiness for college among older-aged students––a common demographic among Israeli students––to identify those most at risk of dropping out.

Conceptual Framework

Tinto’s (1975) theory of student departure and Bean’s (1980, 1983) model of student attrition have generated a long-standing theoretical impact (see reviews by Aljohani, 2016; Burke, 2019; Burrus et al., 2013). Although derived from different theoretical perspectives, they share common ground. Aljohani (2016) suggests that the most influential variables regarding student attrition are the quality of the student’s experience of the institution and their academic and social integration into the institution. Burrus et al. (2013) cited eight factors relating to persistence in college: (a) institutional environmental factors, (b) student demographic characteristics, (c) commitment, (d) academic preparation and success, (e) psychosocial and study skills, (f) integration and fit, (g) student financial resources, and (h) environmental pull factors. These factors can be subsumed under three categories (Brewer et al., 2021): factors placing the student on track (e.g., preparation, motivation), factors keeping the student on track (e.g., self-management, social support), and factors pushing the student off track (e.g., out-of-class stressors). As the first semester proceeds, the interaction between these factors affects academic performance and, thus, persistence (Burrus et al., 2013).

Carroll et al. (2009) and Bowles and Brindle (2017) classified the factors that may facilitate or inhibit student retention into three groups: (a) situational factors (e.g., work and family obligations and other life events), (b) institutional factors (e.g., policy, financial aid, and support services), and (c) dispositional factors relating to student characteristics (e.g., age, parental education, motivation, self-efficacy and social engagement). The focal point of this paper is the internal characteristics of students (“pull factors” or “dispositional factors”; Brewer et al., 2021) at a specific critical period– students’ initial transition into college (Casanova et al., 2021). Thus, the present paper focuses on the early detection of students' college readiness and its associations with retention.

College Readiness

Lapan et al. (2012) claimed that “failure to persist in college is a critical college readiness issue” (p. 59). College readiness denotes factors that predict key college outcomes––persistence and achievement (Gore et al., 2019; Komarraju et al., 2013; Radunzel & Noble, 2012). Three types of college outcome predictors have been identified: (a) measures of scholastic aptitude, such as standardized test scores and high school grade point average (GPA); (b) demographic predictors, such as gender, SES, and ethnicity; and (c) psychosocial factors, such as academic self-efficacy, motivation to study, and social skills (Gore et al., 2019; Robbins et al., 2006).

Academic Aspects of College Readiness

Academic readiness has been considered the traditional and primary indicator of college success (Komarraju et al., 2013). It denotes academic aptitude and cognitive abilities associated with persistence and the quality of academic performance (e.g., GPA; Richardson et al., 2012). Standardized scholastic aptitude tests combined with high school GPA and advanced mathematics and science classes were found to predict first-year college GPA and first-to-second-year retention (Huh & Huang, 2016; Robbins et al., 2006). However, studies have shown that scholastic aptitude scores and high school grades account for only 20–25% of the variance in first-year college GPA (e.g., Huh & Huang, 2016; Richardson et al., 2012), highlighting the effects of other factors predicting college success and retention (Richardson et al., 2012).

Demographic Variables and College Readiness

Pre-entry characteristics of students (e.g., gender, age, SES, parents' education, and ethnicity) are incorporated in most models for student persistence (Chung et al., 2017; Pascarella & Terenzini, 2005; Tinto, 1997). Relative to their demographic cohorts, older students (Gilardi & Guglielmetti, 2011), male students (Conger & Dickson, 2017; Ginder et al., 2017; OECD, 2021), low-SES students, and FGCSs are less likely to enroll in college (Choy, 2001) and demonstrate lower persistence rates (King & Ndum, 2017; Radunzel & Noble, 2012).

The number of non-traditional students attending college has increased in recent decades (Biddix et al., 2021; Chung et al., 2017). These students often begin college less academically prepared (Radunzel & Noble, 2012), have more financial difficulties (Martin et al., 2013), and struggle to adjust to college’s social and cultural aspects (Collier & Morgan, 2008).

A particular group of interest is older students; one-third of US undergraduate students are older than 25 (Markle, 2015). These students may be less committed to college and struggle to adapt to university life due to financial difficulties, family commitments, or job responsibilities (Van Rhijn et al., 2016). Conversely, many benefits accrue to those beginning college at an older age: Their maturity and experiences boost their ability to succeed and persist in their studies (Chung et al., 2017; Johnson & Nussbaum, 2012). Older students are likely to have a better self-regulating capacity that facilitates coping with college stressors (Johnson & Nussbaum, 2012), making them more resilient to stress (Chung et al., 2017), a factor also contributing to retention and GPA (Allan et al., 2014). Beginning college and adapting to a new way of life following a relatively lengthy break from formal education is challenging and requires significant adjustment (Casanova et al., 2021).

Whereas beginning higher education at an older age is a feature unique to Israeli students, deferring enrollment in higher education in other countries has become increasingly common (Guidi, 2018). However, compared with other countries, the reason for deferred enrollment for Israeli students (i.e., mandatory military service) does not necessarily indicate an underlying risk factor (SES, FGCS, age, gender, or academic ability), and the delay is more comparable to countries with mandatory military service (e.g., Finland, Switzerland, and Greece; Kosonen et al., 2019) or vocational education tracks (e.g., in Germany; Tieben, 2020).

Psychosocial Readiness for College

Substantial research has addressed college persistence and academic performance by examining non-cognitive factors (for reviews, see Richardson et al., 2012; Robbins et al., 2004). Beyond adequate scholastic abilities, college readiness denotes a set of behaviors, attitudes, and psychological constructs that facilitate coping with the challenges and demands of higher education (Lombardi et al., 2011). Psychosocial factors are academically-related personality facets that tap into broader personality constructs (Peterson et al., 2006), such as academic self-efficacy (Richardson et al., 2012), effort regulation (Richardson et al., 2012), motivation (Baars & Arnold, 2014), social self-efficacy (Elliott, 2016), social skills (Kahn et al., 2002), grit (Bowman et al., 2015), psychological stress (Allan et al., 2014), and time management skills (Krumrei-Mancuso et al., 2013). Psychosocial college readiness facilitates persistence and higher GPA in college, particularly among first-year students (King & Ndum, 2017; Robbins et al., 2004; 2006). A significant feature of psychosocial factors is that they can be enhanced through educational interventions (Fong et al., 2017; Gore et al., 2019). Thus, assessing psychosocial factors before beginning college presents an opportunity to address and diminish risk factors, thus promoting college readiness (Krumrei-Mancuso et al., 2013). Whereas this paper focuses on psychosocial factors, other retention facilitators and barriers need to be acknowledged (e.g., Bowles & Brindle, 2017). As Burrus et al. (2013) noted, “emphasis on stress and coping is much more applicable to the everyday, real-life experiences of students of all demographic characteristics and students attending different types of institutions” (pg. 36).

Measuring Psychosocial Readiness for College

Based on a meta-analysis of psychosocial factors predicting first-year persistence and GPA, ten broad constructs were identified (Robbins et al., 2004) and incorporated into the Student Readiness Inventory (SRI; Le et al., 2005): Academic Discipline, Academic Self-Confidence, Commitment to College, Communication Skills, Emotional Control, General Determination, Goal Striving, Social Activity, Social Connection, and Study Skills. The SRI has been shown to successfully identify students at risk of dropping out (King & Ndum, 2017).

A more recent measure, the Student Strength Inventory (SSI; Gore et al., 2019), comprises six of the ten SRI factors found to be the best predictors of college outcomes, albeit labeled slightly differently: Academic Self-Efficacy, Academic Engagement, Campus Engagement, Resiliency, Social Comfort, and Educational Commitment. Gore et al. (2019) reported the SSI’s adequate internal consistency and construct validity, highlighting its advantage as a brief and applicable tool for detecting students at risk of dropping out of college. Another measure, the College Learning Effectiveness Inventory (CLEI; Kim et al., 2010), comprises six psychosocial scales: Academic Self-Efficacy, Organization and Attention to Study, Stress and Time Press, Involvement with College Activity, Emotional Satisfaction, and Class Communication. The CLEI scales explained 16.3% of the variance in first-semester GPA beyond demographic variables (Krumrei-Mancuso et al., 2013). Despite these measures’ validity and utility, they focus on the respondents’ recent high school experiences. They are thus less suited for assessing the psychosocial readiness for college of Israeli young adults, who are typically older and have experienced a prolonged gap from formal education. Also, given the growing number of older students in other countries, the existing measures may not be appropriate for them as well.

Higher Education in Israel

Unlike young adults in most Western countries, Israeli youth typically do not enter college immediately following their high school graduation. Most serve in the mandatory military (2–3 years) or the alternative optional national civil service (1–2 years). Following their service, many work for several months to save money for a lengthy (typically 6–12-month) backpacking trip and for college (Lipshits-Braziler et al., 2015). Consequently, Israeli first-year students are characteristically older (23–24) than their counterparts in many other countries. Furthermore, Israeli college applicants are required to rank-order the major(s) they wish to study. For most Israeli colleges, admission criteria include their high school GPA (HSG) and a scholastic aptitude test (the Psychometric Entrance Test [PET], the Israeli equivalent of the SAT or ACT), with different weighting and cut-off criteria for the various majors. Thus, most students in Israel are admitted to a particular major and, thus, are likely to have demonstrated the required scholastic aptitude to succeed in their chosen major; however, this screening notwithstanding, not all students remain in college.

Assessing psychosocial readiness for college has yet to be developed in Israel. Only few studies have highlighted the role of non-cognitive factors on persistence in higher education in Israel. Davidson et al. (2011) reported that interventions that improved the individual’s sense of coherence, self-efficacy, and hope enhanced students’ academic adjustment, as reflected in their grades. Lufi et al. (2003) found that personality factors did not distinguish between college persisters and dropouts among Israeli students. They attributed Israeli students’ relatively low dropout rates (8.7% [Central Bureau of Statistics; CBS, 2018], relative to 12% in OECD countries [OECD, 2022] and 18% in the USA [NCES, 2022]) to their older age and greater maturity.

The Present Research

Responding to the call for a multidimensional assessment of psychosocial readiness for college (Gore et al., 2019; Robbins et al., 2004), the present research aligns with Gore et al.’s (2019) suggestion to assess only those dimensions that contribute significantly to first-year college achievement and persistence. We adapted and modified the SSI’s multidimensional approach and applied it to first-year college students about to begin college several years after high school graduation. We report five studies, using five different and large samples of first-year college students, that review the development, psychometric properties, and validity of the Psychosocial Readiness for College questionnaire (PRCq), targeting students starting college in their 20s’. Specifically, we describe the development of the PRCq, a multi-dimensional assessment of psychosocial readiness for college, test its psychometric properties (including exploratory and confirmatory factor analysis and measurement invariance), as well as its convergent and discriminant validity against personality and scholastic measures. We also tested the PRCq’s predictive validity, adopting the criterion of first-year retention.

Study 1: The Development of the Psychosocial Readiness for College Questionnaire

Study 1 aimed to develop the PRCq and explore its six-dimensional structure.

Method

Developing the Psychosocial Readiness for College Questionnaire

We conducted a pilot study to test the initial version of the PRC questionnaire (PRCq), comprising 50 items distributed among six scales. Five of the scales correspond to the SSI scales (Gore et al., 2019): Academic Self-Efficacy, Educational Commitment, Social Comfort, Campus Engagement, and Resiliency. SSI’s sixth scale, Academic Engagement, addressed prioritizing academic tasks related to current high school behaviors (e.g., “I turn my work in on time”, “I often go to class without being fully prepared”). Following a content analysis of the items, we relabeled this scale as Self-Discipline, defining it as the degree to which the individual follows through on commitments and obligations (e.g., “When I make a plan, I can stick to it”), reflecting the individuals’ control over their everyday activities. Thus, we replaced the SSI’s Academic Engagement scale with Self-Discipline in the PRCq.

We reviewed all the SSI items and adjusted them to fit the Israeli higher education setting, taking into account the unique features of older student populations. For example, Academic Self-Efficacy items in the SSI refer to first-year academic achievements common in the American college curriculum, such as required courses in science and English. These items were replaced by general Academic Self-Efficacy items; for example, instead of “I will be able to complete college English requirements with a B or better,” we included an item of general academic self-efficacy: “I am confident I can attain a high GPA.” Finally, the Campus Engagement-scale items were revised and adapted to fit Israeli campus life. For instance, items relating to “joining campus clubs” and “participating in orientation activities” are less common on Israeli campuses; thus, we replaced these with “joining student union activities” and “being involved in campus activities.” These 50 items were administered to 195 students before the beginning of their first year at a research university in October 2011. Based on the results of an exploratory factor analysis and the internal consistency reliability analysis, we deleted ten items and revised 12 others. A second 40-item pilot questionnaire (N = 975) was then administered, resulting in removing four items. Finally, to further shorten the questionnaire, we removed six items (one from each scale) that did not significantly contribute to the scales’ internal consistency reliabilities. Thus, the final version of the PRCq comprised 30 items, five per each scale, in addition to one warm-up and two validity items.

Participants and Procedure

After obtaining the approval of the Institutional Review Board and the Student Administration of a research university, a new sample was recruited to test the structure of the PRCq. We emailed an invitation to the university’s 3,284 first-year students two weeks before the beginning of the academic year. Students agreeing to participate clicked on a link to a brief (6–8-minute) online questionnaire regarding “first-year students’ perceptions of college.” The response rate was 40%. Of those completing the questionnaire, we deleted 84 participants: 62 participants reporting less than 12 or more than 13 years of education, 5 participants younger than 18 or older than 30, and 17 participants who completed the questionnaire in less than 120 seconds or more than 30 minutes. Thus, the final sample included 1,239 university students (58.4% female). The analyses included only students aged 18–30 (M = 22.59, SD = 1.91) reporting12 -13 years of education (M = 12.06, SD = 0.23). We limited the participation age to 30 because we were interested in first-time college students, and many of those above 30 also reported previous post-secondary education.

Instruments

Psychosocial Readiness for College Questionnaire

The final version of the PRCq comprises 30 items, five for each of the six scales: Academic Self-Efficacy (e.g., “I am confident that I can maintain a high GPA”), Educational Commitment (e.g., “I am confident that attaining a college degree is the right goal for me”), Social Comfort (e.g., “I enjoy meeting new people”), Campus Engagement (e.g., “Being involved in campus life is important for my personal development”), Self-Discipline (e.g., “I can be counted on to maintain a schedule”), and Resilience (e.g., “I am easily stressed, even by things that are not really important”; reverse-scored). Participants were asked to rate the extent to which each item describes them on a 7-point Likert-type scale, ranging from 1 (does not describe me at all) to 7 (describes me very well). The warm-up item was: “I have high expectations of my academic studies.” Two validity items were embedded to detect inattentive responses (“I am very pleased when I do well on my exams” and “I don’t mind failing an exam”). One Self-Discipline scale item (“I tend to put off tasks to the last minute”) and all Resilience scale items are reverse-scored. In the present sample, the Cronbach alpha (Cα) internal consistency reliabilities of the six scales were high (.92, .86, .84, .91, .82, and .79, for the six scales, respectively).

Background Questionnaire

Participants were asked to report their age and years of education. We solicited no information regarding the participants' ethnic or religious affiliation, as asking such questions is viewed by many Israelis as invasive.

Results

The 30 PRCq items were factor analyzed using principal component analysis with Promax with Kaiser normalization as the rotation method. The results yielded six factors with an eigenvalue greater than 1, explaining a total of 67.7% of the variance. Appendix A shows that all items loaded on the expected factor (loadings ranged from .67 to .92), and no cross-loadings were observed. The factor contributing the highest variance was Academic Self-Efficacy (26.9%), followed by Social Comfort (12.1%), Educational Commitment (10.6%), Campus Engagement (6.7%), Resiliency (6.2%), and Self-Discipline (5.4%).

Study 2: Confirmatory Factor Analysis and Measurement Invariance of the Psychosocial Readiness for College Questionnaire

Study 2 aimed to verify the six-dimensional structure of the PRCq that emerged in Study 1 by using confirmatory factor analysis and measurement invariance analyses for students from different groups and backgrounds. The effect of socio-demographic variables on academic readiness has been explored extensively (e.g., Chung et al., 2017). Numerous prevention and intervention programs to promote persistence have addressed diverse student populations, designing programs emanating from the students’ socio-demographic characteristics (Jury et al., 2017). After supporting the PRCq’s measurement invariance, an additional goal of Study 2 was to examine whether the six PRC dimensions differ across four demographic variables (gender, age, socioeconomic status [SES], and first-generation college students [FGCS]) and the type of higher education institution (university vs. college)).

Differences Between Groups in Demographic Characteristics

Age

Older students often have greater ambivalence regarding their commitment to higher education due to time constraints imposed by family and job responsibilities (Gilardi & Guglielmetti, 2011; Van Rhijn et al., 2016). Those living off-campus have more difficulty adapting to university life and benefiting from university support, often impeding their integration and engagement in college life (Gilardi & Guglielmetti, 2011). At the same time, older students may benefit from greater psychosocial readiness due to their maturity, and their life experience may contribute to higher resilience and more successful coping with stress (Chung et al., 2017; Johnson & Nussbaum, 2012), two of the factors associated with persistence and scholastic performance (Allan et al., 2014). Hence, we hypothesized that relative to younger students, older students would score lower on Educational Commitment (h1) and Campus Engagement (h2) but higher on Social Comfort (h3), Self-Discipline (h4), and Resilience (h5). Considering that studies on Academic Self-Efficacy have proven inconclusive, we do not posit an age-related hypothesis regarding this scale.

Gender

Gender distinctions have been reported regarding college behavior. Thus, women demonstrate higher attendance rates (Conger & Dickson, 2017; OECD, 2021), GPA (Richardson et al., 2012), persistence, and graduation rates (Ginder et al., 2017; OECD, 2021). Le et al. (2005) found that women scored higher than men on commitment to college, communication skills, and academic discipline. Gore et al. (2019) reported that women scored higher on five of the six dimensions of the Student Strengths Inventory; only on Resilience did men score higher. Given these reported findings, we hypothesized that women would score higher than men on all but one of the PRCq scales (h6) but lower than men on Resilience (h7).

Socioeconomic Status

Socioeconomic Status (SES) is typically derived from family income and parents’ educational attainment (Choy, 2001; Grebennikov & Shah, 2012). For low-SES students and those with diverse backgrounds, non-cognitive factors are especially important contributors to college success (Sedlacek, 2011). Low-SES students are likely to face unique psychological barriers, often characterized by greater emotional distress, negative self-perceptions, and an inclination toward avoidance motivation (Jury et al., 2017). However, other studies have stressed the high motivation and determination of low-SES students who enroll and persist in college despite encountering obstacles (Mckay & Devlin, 2016; Sedlacek, 2011). Psychosocial factors may compensate for inadequate academic readiness (Komarraju et al., 2013). Le et al.’s (2005) study found low associations between ethnicity, family income, and psychosocial factors. Thus, we expected to find small or negligible differences in PRCq scores between low- and high-SES students (h8).

First-Generation College Students

First-generation college students (FGCS) are students whose parents did not earn a college degree. This group often corresponds with SES (Fiske & Markus, 2012) and ethnicity (Harackiewicz et al., 2016). Given their lack of “college knowledge,” FGCS may have greater difficulty integrating into their new social environment, such as accommodating instructors’ expectations or interacting with professors (Collier & Morgan, 2008), and therefore, may experience higher levels of uncertainty about fitting into higher education (Harackiewicz et al., 2016). To take on the role of a college student, FGCS need to contend with relatively more potential stressors (Garriott & Nisle, 2018), making this role more challenging for those whose families lack such experience (Collier & Morgan, 2008). Accordingly, we hypothesized lower PRCq scores for FGCS in Social Comfort (h9) and Campus Engagement (h10).

College vs. University Students

Over the past three decades, Israel’s higher education system has expanded significantly, increasing access to higher education among students from geographically peripheral towns and low-SES backgrounds (Shwed & Shavit, 2006). This change is apparent in the increased number of public and private colleges and, to a lesser extent, research universities. Universities’ emphasis on excellence (Ayalon et al., 2008) is reflected in more selective admission requirements (CBS, 2017). We hypothesized that university students would score higher on the PRCq than college students (h11).

Method

Participants and Procedure

As in Study 1, an invitation email was disseminated to two cohorts of first-year students just before the beginning of the academic year; 4,081 and 4,097 students from a single research university, and 5,049 and 6,671 students from eight and ten academic colleges in Israel, respectively. The response rate was 34% and 24.4% for the university cohorts, and 16.9% and 19.4% for the college cohorts. The final sample included 2,126 university students (58.2% women) and 2,147 college students (65.3% women). The overall mean age was 23.61 (SD = 2.15) for men and 22.42 (SD = 1.83) for women, respectively. Although a small minority of students (about 11%) are younger (i.e., < 21) than the target population of students, we decided to include them in the analyses because the goal was to develop an easily administered assessment that can be used on the entire cohort and not only for those who begin college in their twenties.

The participants were divided into two age groups, two SES groups, and two groups of college generation status. Concerning age, participants were divided into younger students (18–22 for women, 18–23 for men; 48.7%) and older students (23–30 for women, 24-30 for men; 51.3%); the difference in the cut-off points between men and women stems from the differential military service length in Israel (two years for women and three years for men).

SES level was based on the Israel Central Bureau of Statistics (CBS, 2019) classification of Israel’s towns according to 10 SES clusters, ranging from 1 (lowest SES) to 10 (highest SES). The SES code assigned to the town reported as the site of the participants’ elementary school was used for determining the participants’ SES level. For the current analysis, the SES clusters were divided into two groups: low-SES clusters (1-5; 40.7%) and high-SES clusters (6-10; 59.3%). More low-SES students were enrolled in the university (45.7%) than in colleges (35.7%), χ² (1, N = 3900) = 40.94, p < .001. Finally, we distinguished between two additional groups: FGCS (28.8%) and non-FGCS (71.2%); more FGCS were enrolled in colleges (36.1%) than in the university (21.7%), χ² (1, N = 4006) = 101.78, p < .001.

Instrument

Psychosocial Readiness for College Questionnaire (PRCq)

As in Study 1, participants responded to the PRCq; The median Cα of the six scales was .86 (ranging from .81 to .91).

Background Questionnaire

Participants were asked to report their age, years of education, parents' education (undergraduate or above, post-high school professional training, high school diploma, or less than 12 years of education), and town of residence during elementary school to determine their SES (reported by 91.2% of the participants). We asked for participants’ elementary school location to indicate SES rather than high schools because elementary schools are much more likely to be near their home. FGCS was defined as a student, both of whose parents did not complete an undergraduate degree.

Results

Confirmatory Factor Analysis

We carried out a confirmatory factor analysis (CFA) on the sample of 4,273 first-year students to test the data’s goodness of fit for the 6-scale structure. According to the hypothesized model (H: 30–6), the PRCq’s 30 items can be aggregated into six intercorrelated scales. The first alternative model (A1: 30–6–1) signifies that the 30 items can be aggregated into the six scales and then combined into a single higher-order factor. The second alternative model (A2: 30–1) denotes that the 30 items represent a single factor. The goodness of fit of the models was evaluated according to the following criteria: (a) a comparative fit index (CFI) greater than .90 (Kline, 2005); (b) RMSEA - root mean square error of approximation is less than .06 (Brown, 2006), and (c) SRMR – standardized root mean square residual less than .08 (Hu & Bentler, 1999). The results of the CFA are presented at the top of Table 1. As can be seen, the hypothesized model (H1: 30–6) resulted in better fit indices on all fit criteria (CFI = .937, RMSEA = .052 [.051–.053], SRMR = .051) compared with the alternative models. The fit of the A1:30–1 was unacceptable: (CFI = .388, RMSEA = .158 [.157–.159], SRMR = .149), and the fit of the A2:30–6–1 model was good (CFI = .925, RMSEA = .056 [.054–.057], SRMR = .072) but less than that the H1:30–6 model. In summary, these results support the theoretical model of psychosocial readiness comprising six distinct dimensions.

Table 1.

Confimatory Factor Analysis and Measurement Invariance for the PRCq Structure (N = 4273) across Gender, Age, SES, (non)FGCS, and Institution Type.

	x²	df	CFI	RMSEA	SRMR	ΔCFI	ΔRMSEA	ΔSRMR	Δx²	Δdf	p
H1: 30-6	4866.51	390	.937	.052 [.051-.053]	.051
A1:30-1	43,642.65	405	.388	.158 [.157-.159]	.149	−.548	.106	.098	38,776.00	15	.000
A2:30-6-1	5687.43	399	.925	.056 [.054-.057]	.072	−.011	.004	.021	820.93	9	.000
Gender
Configural	5377.12	780	.935	.053	.051
Metric	5440.69	804	.934	.052	.051	−.001	−.001	.001	63.57	24	.000
Scalar	5553.58	828	.933	.052	.052	−.001	.000	.000	112.89	24	.000
Strict	5710.54	858	.931	.051	.052	−.002	.000	.000	156.96	30	.000
Age
Configural	5362.28	780	.935	.052	.050
Metric	5400.27	804	.935	.052	.051	.000	−.001	.001	37.99	24	.035
Scalar	5549.77	828	.933	.052	.052	−.002	.000	.001	149.51	24	.000
Strict	5754.95	858	.930	.052	.052	−.002	.000	.001	205.17	30	.000
SES
Configural	4981.69	780	.935	.053	.051
Metric	5003.71	804	.935	.052	.051	.000	−.001	.000	22.02	24	.578
Scalar	5090.95	828	.934	.051	.052	−.001	.000	.000	87.24	24	.000
Strict	5165.51	858	.933	.051	.052	−.001	−.001	.000	74.56	30	.000
(non)FGCS
Configural	5058.46	780	.936	.052	.051
Metric	5110.87	804	.935	.052	.051	.000	−.001	.001	52.40	24	.001
Scalar	5232.76	828	.934	.052	.052	−.001	.000	.000	121.90	24	.000
Strict	5335.67	858	.933	.051	.052	−.001	.000	.001	102.90	30	.000
Institution
Configural	5369.76	780	.935	.052	.051
Metric	5409.71	804	.935	.052	.051	.000	−.001	.000	39.95	24	.022
Scalar	5723.37	828	.931	.053	.052	−.004	.001	.001	313.66	24	.000
Strict	5965.71	858	.928	.053	.053	−.003	.000	.001	242.34	30	.000

Note. Gender (N_Men = 1634; N_Women = 2639), Age (N_Younger = 2083; N_Older = 2190), SES (N_LowSES = 1589; N_HighSES = 2311), (non)FGCS (N_FGCS = 1152; N_NON-FGCS = 2854), Institution Type (N_university = 2126; N_Colleges = 2147).

Measurement Invariance

To assess the psychometric equivalence of the PRCq construct across groups, we examined the measurement invariance of the hypothesized PRC model for each of the five independent variables: gender, age, SES, FGCS, and institution type. The CFI goodness-of-fit index and RMSEA were then used to examine the change in fit across groups (Cheung & Rensvold, 2002). Measurement invariance is determined when the difference in CFI in the model fit is under .01, and the difference in RMSEA is under .015 (Cheung & Rensvold, 2002). The results, presented in Table 1, supported measurement equivalence, as the PRCq scale scores demonstrated strict invariance for all five independent variables: gender (CFI ≥ .931 and RMSEA ≤ .053), age (CFI ≥ .930 and RMSEA ≤ .052), SES (CFI ≥ .933 and RMSEA ≤ .053), FGCS (CFI ≥ .933 and RMSEA ≤ .052), and institution type (CFI ≥ .928 and RMSEA ≤ .053). The differences in the goodness-of-fit statistics are all under .01 (ΔCFIs ≤ |.004| and ΔRMSEAs ≤ |.001|).

Group Differences

To further examine differences in the six PRCq scales in each independent variable, subsequent t-tests were conducted with a Bonferroni correction for multiple comparisons (corrected α = .008). Table 2 presents the results of the t-tests. For age, younger students scored higher than older students on Academic Self-Efficacy (d = 0.14), Educational Commitment (d = 0.32), and Self-Discipline (d = 0.24), and lower on Social Comfort (d = −0.15) and Resilience (d = 0.16), supporting Hypotheses h1, h3, and h5. Interestingly, the age difference in Self-Discipline (h4) was not supported, with results reflecting the reverse of our hypothesis. Due to the lack of difference between older and younger students on Campus Engagement, Hypothesis h2 was not supported. For gender, women scored significantly higher than men on Educational Commitment (Cohen’s d = 0.24), Campus Engagement (d = 0.12), and Self-Discipline (d = 0.15), but lower on Resilience (d = −0.34), thus supporting Hypothesis h7 and partially supporting Hypothesis h6.

Table 2.

Group Differences in the PRCq Scales (N = 4273) across Gender, Age, SES, (non)FGCS, and Institution Type.

	Academic Self-efficacy		Educational Commitment		Social Comfort		Campus Engagement		Self-Discipline		Resilience
Gender	M	SD	M	SD	M	SD	M	SD	M	SD	M	SD
Men _{(N = 1,634)}	5.79	0.86	6.16	0.92	5.27	1.15	4.45	1.33	5.17	1.05	4.33	1.32
Women _{(N = 2,639)}	5.76	0.88	6.37	0.83	5.31	1.11	4.60	1.23	5.33	1.06	3.89	1.30
t ₍₄₂₇₁₎	1.25		−7.70^***		−0.96		−3.68^***		−4.77^***		10.89^***
d	0.04		0.24		0.03		0.12		0.15		0.34
Age
Younger _{(N = 2,083)}	5.84	0.88	6.43	0.75	5.21	1.16	4.59	1.27	5.40	1.03	3.95	1.34
Older _{(N = 2,190)}	5.71	0.87	6.16	0.95	5.38	1.08	4.49	1.27	5.14	1.07	4.16	1.31
t₍₄₂₇₁₎	4.68^***		10.34^***		−4.87^***		2.52^*		7.91^***		−5.09^***
d	0.14		0.32		0.15		0.08		0.24		0.16
SES
Low _{(N = 1,689)}	5.82	0.87	6.37	0.79	5.23	1.14	4.50	1.29	5.32	1.03	4.09	1.33
High _{(N = 2,311)}	5.75	0.88	6.24	0.91	5.35	1.11	4.59	1.26	5.24	1.07	4.07	1.34
t₍₃₈₉₈₎	2.45^*		4.63^***		−3.31^***		−2.16^*		2.36^*		0.49
d	0.08		0.15		0.11		0.07		0.08		0.02
(non)FGCS
FGCS _{(N = 1,152)}	5.80	0.90	6.41	0.77	5.29	1.16	4.57	1.32	5.32	1.05	4.02	1.33
Non-FGCS _{(N = 2,854)}	5.76	0.87	6.25	0.90	5.30	1.11	4.54	1.25	5.26	1.06	4.10	1.33
t₍₄₀₀₄₎	1.02		5.26^***		−0.36		0.57		1.75		−1.75
d	0.04		0.18		0.01		0.02		0.06		0.06
Institution
University _{(N = 2,126)}	5.69	0.89	6.25	0.87	5.18	1.12	4.54	1.23	5.21	1.05	4.09	1.31
College _{(N = 2,147)}	5.85	0.86	6.34	0.87	5.41	1.12	4.54	1.31	5.33	1.06	4.03	1.35
t₍₄₂₇₁₎	−6.24^***		−3.46^***		−6.87^***		−0.06		−3.64^***		1.45
d	0.19		0.11		0.21		0.00		0.11		0.04

Note. SES = Socio-Economic Status, (non)FGCS = (non) First Generation College Student.

*p < .05; ** p < .01; *** p < .001.

Low-SES students scored higher than high-SES students on Educational Commitment (d = 0.15) but lower on Social Comfort (d = 0.11), thus supporting Hypothesis h8. FGCS scored higher than non-FGCS only on Educational Commitment (d = 0.18); this finding was not hypothesized. The hypothesized lower PRCq scores for FGCS in Social Comfort (h9) and Campus Engagement (h10) were not supported. Finally, for institution type, college students scored higher than university students on Academic Self-Efficacy (d = 0.19), Educational Commitment (d = 0.11), Social Comfort (d = 0.21), and Self-Discipline (d = 0.11), findings that do not support Hypothesis h11.

Study 3 – The Associations Between Psychosocial Readiness for College and Scholastic Aptitude

Academic aptitude and cognitive ability have been associated with college outcomes, primarily persistence and first-year GPA (Richardson et al., 2012). Scholastic aptitude tests (e.g., SAT, ACT) and high-school GPA are commonly used as predictors of first-year college GPA and first-to-second-year retention (Gore et al., 2019; Huh & Huang, 2016; Robbins et al., 2006). Whereas these factors have been the traditional predictors of college success, they account for only 20–25% of the variance of first-year college GPA (e.g., Huh & Huang, 2016; Krumrei-Mancuso et al., 2013). Similarly, in Israel, high school grades (HSG) and the Psychometric Entrance Test (PET) accounted for 25% of the variance of first-year college GPA (Kleper & Turvall, 2016).

In Study 3 we explored the association between scholastic aptitude predictors (i.e., HSG and PET) and the six PRCq scale scores. We expected low to negligible associations between the scholastic aptitude factors and the psychosocial factors (h12), reflecting the difference between cognitive and non-cognitive factors associated with persistence (King & Ndum, 2017; Komarraju et al., 2013; Robbins et al., 2004).

Method

Participants and Procedure

We disseminated an email invitation to a new cohort of first-year students at a research university in Israel a month before the start of the academic year (N = 3,456), yielding a response rate of 43%. Participants were asked about their gender, age, and consent to retrieve their admissions data from the university’s student administration office (81% consent). We retrieved these students' HSG and PET scores to examine their association with the PRCq scores. The final sample comprised 1,489 students aged 18–30 with 12–13 years of education (61.8% were women). Their mean age was 21.88 (SD = 1.81) for women and 23.30 (SD = 2.17) for men.

Instrument

Psychosocial Readiness for College Questionnaire

In this study, the median PRCq’s scale score’s Cα was .88, ranging from .81 to .92 for the six scales.

HSG and PET score

Entrance requirements for higher education in Israel include high school matriculation GPA (HSG) and the PET score. In most cases, students are admitted to college based on the combination of HGS (ranging from 0 to 100) and their PET score (ranging from 200 to 800); for certain majors, students can be admitted based on the HGS or the PET score alone. We obtained the HGS of 1,447 students and the PET scores of 1,350 students from the university’s student administration office; the correlation between the HSG and the PET was .47 (N = 1,326).

Results

The correlations between the six PRCq scores and the scholastic aptitude measures are presented on the left-hand side of Table 3. As expected, the correlations were negligible to low (r ≤ |.16|). Of the 12 correlations, six emerged as statistically significant due to the high number of participants: HSG correlated negatively with Academic Self-Efficacy (r = −.07) and positively with Self-Discipline (r = .13). PET correlated negatively with Educational Commitment (r = −.12), Campus Engagement (r = −.08), Self-Discipline (r = −.06), and positively with Resilience (r = .16). These low correlations support the claim that psychosocial factors are distinct from cognitive factors.

Table 3.

Correlations between the PRCq Scale Scores, Scholastic Aptitudes (High School Grades and PET), and BFI Factors.

PRC Scale	Study 3		Study 4 (N =381)
	HSG	PET	BFI
	N = 1447	N = 1350	Ext	Agr	Con	Neu	Ope
Academic self-efficacy	.07^**	−.02	.22^**	.15^**	.30^**	−.32^**	.28^**
Educational commitment	.01	−.12^**	.12^**	.12^*	.29^**	−.07	.02
Social comfort	−.05	−.05	.68^**	.11^*	.24^**	−.24^**	.17^**
Campus engagement	−.01	−.08^**	.33^**	.10^*	.14^**	−.02	.23^**
Self-discipline	.13^**	−.06^*	.23^**	.06	.69^**	−.11^*	.00
Resilience	.01	.16^**	.16^**	.10	.12^*	−.66^**	.03

Note. HSG = High School Grades, PET = Psychometric Entrance Test Score, Ext = Extroversion, Agr = Agreeableness, Con = Conscientiousness, Neu = Neuroticism, Ope = Openness to Experience; bold numbers indicate converging scales.

*p < .05; ** p < .01.

Study 4 - Psychosocial Readiness for College and Big Five Personality Factors

Study 4 aimed to assess the convergent and discriminant validity of the six PRCq scales using the Big Five personality factors: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience (Costa & McCrae, 1992). We selected the Big Five personality traits for validation since personality traits have been linked to psychosocial factors (Peterson et al., 2006). Peterson et al. (2006) found convergent associations between psychosocial readiness assessed by the SRI and the Big Five personality traits; specifically, the SRI’s Emotional Control was associated with Neuroticism, Social Activity with Extraversion, and Academic Discipline with Conscientiousness. Whereas the Big Five traits comprise broad personality domains, SRI traits are narrower, tapping into academic-related characteristics and are better predictors of GPA than the Big Five (Peterson et al., 2006). Richardson et al. (2012) reported associations between the Big Five personality traits and academic performance. They found that low-Conscientiousness students were less likely to persist with challenging work than high-Conscientiousness students and that Neuroticism may impede academic performance due to test anxiety and lower motivation.

These studies suggest some commonality between Extraversion and the PRC’s social dimensions––namely, Social Comfort and Campus Engagement––and between Neuroticism and the PRCq’s Resilience scale (reversed). Hence, we expected moderate to high positive correlations between Extraversion, on the one hand, and Social Comfort and Campus Engagement (h13), on the other, and a moderate negative correlation between Neuroticism and Resilience (h14). Similarly, we expected Conscientiousness to be positively correlated with Self-Discipline and Academic Self-Efficacy (h15). Agreeableness and Openness to Experience would appear to correspond less with the PRC dimensions but may tap into the social domain of Social Comfort (h16); thus, we expected a moderate correlation between the PRCq’s Social Comfort and the Big Five scales of Agreeableness and Openness to Experience.

Method

Participants and Procedure

The PRCq was completed by an additional sample of 1,028 first-year university students at the start of their academic year. Then, approximately 6 months later, we emailed these students, requesting them to complete a questionnaire that included the Big Five Inventory (BFI; John et al., 1991); 631 students completed the BFI. After excluding students due to age (younger than 18 or older than 30), previous education (more than 13 years of schooling), and BFI response time (less than 120 seconds or more than 1,800 seconds), we analyzed the responses of the 381 students who completed both the PRCq and the BFI.

Instruments

The Psychosocial Readiness for College questionnaire

The median internal consistency reliability for the six scales was Cα = .88, with a range of .80 to .92.

Personality Factors - The Big Five Inventory

The BFI is a widely used 44-item instrument of the five-factor personality model (John et al., 1991), with 8–10 items tapping each of the five scales. The items are presented on a 5-point Likert-type response scale, ranging from 1 (disagree strongly) to 5 (agree strongly). The BFI has satisfactory convergent and discriminant relations with other Big-Five-based measures (John & Srivastava, 1999). The BFI’s Hebrew version yielded reliability estimates of Cα = .80, .81, .68, .73, and .76 for Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness to Experience, respectively (Laski, 2002). The current study used the 41-item adapted Hebrew version of the BFI, based on Lipshits-Braziler et al. (2015). Lipshits-Braziler et al. (2015) omitted three items from the Hebrew version of the Openness to Experience factor (tapping artistic and aesthetic experiences) due to unsatisfactory psychometric properties. A subsequent EFA and internal consistency reliability estimates of the BFI’s Hebrew version (N = 631) led us to eliminate five items from the analyses (one item from Agreeableness, Openness to Experience, and Neuroticism, and two items from Conscientiousness), resulting in a 36-item questionnaire with adequate reliability. The internal consistency reliability estimates for the present sample were .84, .73, .80, .83, and .76 for Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience, respectively.

Results

The right-hand side of Table 3 presents the correlations between the six PRCq scales and the five BFI dimensions. As expected, Extraversion was highly positively correlated with Social Comfort (r = .68) and moderately correlated with Campus Engagement (r = .33), supporting Hypothesis h13. Resilience was strongly negatively correlated with Neuroticism (r = −.66), supporting Hypothesis h14. Neuroticism was also moderately negatively correlated with Academic Self-Efficacy (r = −.32). Conscientiousness was highly correlated with Self-Discipline (r = .69) but yielded only weak to moderate correlations with Academic Self-Efficacy (r = .30) and Educational Commitment (r = .29), supporting Hypothesis h15. Openness to Experience had moderate to low positive associations with Academic Self-Efficacy (r = .28) and Campus Engagement (r = .23). Finally, Agreeableness was positively but weakly correlated with the six PRCq scales (r ≤ .15). Thus, the convergent and discriminant validity of the PRCq was supported by its pattern of correlations with the Big Five dimensions.

Study 5 –The Predictive Validity of the Psychosocial Readiness for College Questionnaire

The PRCq was designed as a tool for the early identification of students at risk of dropping out during their first year of college, assessing six psychosocial factors of college readiness. In Study 5, we tested the PRCq’s validity for predicting students’ academic status at the end of the first year: Did these first-year students persist or drop out?

Method

Participants and Procedure

The participants in this study were the students from the two cohorts of first-year university students who completed the PRCq in Study 2. After reporting general demographic information, the students were asked if they consented to link their responses on the PRCq with additional information from the Student Administration Office. The following analyses examine the data of the 1,695 university students who selected “Yes, I agree” (80% of the students). For these students, we obtained information from the Student Administration regarding whether they were registered as students for the following academic year (or dropped out).

Preliminary Analyses

For each student, we calculated the six PRCq scale scores. First, we standardized the six raw scores to Z-scores within each PRCq scale. Next, for each scale, we classified students as “at risk” for dropping out if their Z score was < −1.5. Thus, participants were assigned six dichotomous risk classifications (“ready” or “at risk”) according to their scores in each of the PRCq scales. Then, we assigned two dropout risk indicators to each participant. The first indicator comprised two classifications: at risk for dropping out based on one or more PRCq scales (“1”) or not at risk (in any scale; “0”). The second indicator comprised three classifications: at high risk for dropping out based on low scores in two or more PRCq scales (“2+”), at moderate risk, being at risk due to a low score in only a single PRCq scale (“1”), or not at risk (no low score in any scale; “0”).

Results

Table 4 presents the number of students “at risk” of dropping out (i.e., Z < -1.5 in one or more scales) and the number of students psychosocially “ready for college” for each of the six PRCq scales and whether they persisted or dropped out. For each scale, the table also presents the χ² tests comparing the expected dropout rates––as derived from the PRCq––with the observed numbers in both groups. As seen in Table 4, in three of the PRCq’s scales––Educational Commitment, Social Comfort, and Campus Engagement––the dropout rate was two to three times higher among at-risk students than among those not classified at risk (9.92% vs. 5.50%, 15.08% vs. 5.10%, and 12.93% vs. 5.32%, respectively). For two scales––Academic Self-Efficacy and Self-Discipline––the difference between the observed dropout rate of students at risk and those not at risk was small, but in the hypothesized direction, and for the Resilience scale, the difference was negligible.

Table 4.

The Associations between the Predicted and the Observed Frequencies of Dropout among First-Year Students (N = 1695).

PRCq Scale	Prediction	n Observed		%	χ²
		Dropout	Persisted	Dropout	(p)
		n = 99	n = 1596	Dropout	(p)
Academic	Psychosocially ready	89	1494	5.62	2.079
Self-efficay	At risk	10	102	8.93	(.149)
Educational	Psychosocially ready	86	1478	5.5 0	4.304
Commitment	At risk	13	118	9.92	(.038)
Social comfort	Psychosocially ready	80	1489	5.10	21.125
Social comfort	At risk	19	107	15.08	(.001)
Campus	Psychosocially ready	84	1495	5.32	11.383
Engagement	At risk	15	101	12.93	(.001)
Self	Psychosocially ready	90	1470	5.77	0.182
Discipline	At risk	9	126	6.67	(.670)
Resiliance	Psychosocially ready	90	1452	5.84	0.001
Resiliance	At risk	9	144	5.88	(.982)
N of risk	None	57	1118	4.85	6.821
Factors	1 or more	42	478	8.08	(.009)
N of risk	None	57	1118	4.85	10.017
Factors	1 risk factor	23	318	6.74	(.007)
Factors	2+ risk factors	19	160	10.61

Note: At risk for each of the scales was defined as a Z-score below 1.5 sd. Statistically significant associations between being at risk and actual dropping out are highlighted in bold.

Next, we compared the dropout rates of those “at risk in one or more scales” with participants that were “ready for college” in all six scales, presented at the bottom of Table 4. The results show, as expected, that the dropout rate (8.08%) was higher among “at risk” students based on one or more scales than the “ready for college” students (4.85%) in all scales. Finally, we conducted an additional comparison among the following three groups: (a) high risk of dropping out, (b) moderate risk of dropping, or (c) not at risk of dropping out. As seen in the lowest section of Table 4, the dropout rates differed among the three groups. Those with two or more risk factors were 2.19 times as likely to drop out of college (10.61%) than students with no risk factors (4.85%), χ² (1, N = 1354) = 9.739, p < .01). Although the dropout rate among those with two or more risk factors was 1.57 times higher than those with only one risk factor (6.74%), this difference was not statistically significant, χ²(1, N = 520) = 2.367, p = .124.

To examine the sensitivity of the results to the chosen cut-off point, we repeated the analyses in Table 4 with a Z cut-off score of below −2.0. The results resembled those reported in Table 4. However, a noteworthy difference emerged regarding the effect of the number of risk factors on the dropout rate: With a cut-off point of −2.0 SD, the percentage of dropouts increased from 10.61% to 16.07%, highlighting those who are at high risk of dropping out due to very low readiness (i.e., Z below −2 SD) in two or more scales.

General Discussion

The present research aimed to develop and test the Psychosocial Readiness for College questionnaire to assess psychosocial factors related to college readiness, targeting students in their early twenties and older. We developed and then examined the PRCq on five large samples of first-year college students in Israel. Analysing data from five cohorts, we tested the PRC’s construct validity, measurement invariance, convergent and discriminant validity (using high school GPA, PET, and the Big Five personality traits), and predictive validity (using the criterion of students' retention rate after one year). Developing a measure of psychosocial readiness for older students who have deferred enrollment to higher education and who have already chosen their major(s) is crucial for facilitating their successful transition into higher education. Early identification of students at risk of dropping out due to inadequate psychosocial readiness can facilitate interventions addressing modifiable risk factors related to dropping out, beginning them as early as feasible, before students encounter the common college challenges (e.g., falling behind, failing exams, feeling stressed) that may lead to dropping out.

The findings supported the PRCq’s psychometric qualities: the scales' reliabilities and the questionnaire’s factor structure. The results of the CFA supported the PRC’s six-scale structure, yielding fit indices better than an alternative structure comprising a single higher-order construct of general psychosocial readiness for academic studies. This finding aligns with reports on the SSI (Gore et al., 2019) and the CLEI (Krumrei-Mancuso et al., 2013). The PRCq targets a wider range of relatively older prospective students, specifically those deferring their academic pursuits following high school graduation. The previously cited assessments were designed for prospective students still in high school or at the early stages of their college studies, often before deciding on their major. Consequently, these assessments relied upon students' recent high school or early college experiences (e.g., Krumrei-Mancuso et al., 2013; Le et al., 2005; Gore et al., 2019). The increasing frequency of students taking gap years between high school and college and the increased enrollment of non-traditionally-aged students (Brock, 2010) highlights the importance of a later-age-relevant assessment in Israel and other countries.

Further examination of the PRCq concerning various background variables supported its measurement invariance across age, gender, SES, non/FGCS, and institution type. This enabled us to examine group differences that yielded small to moderate effect sizes. Interestingly, “birth factors” (age and gender) manifested larger differences in the PRCq scores (median effect size 0.15) than “family factors” (SES and non/FGCS; median 0.07). As interventions cannot modify birth or family circumstances, these findings highlight the importance of focusing on individual differences in psychosocial dimensions within these groups to identify individuals at greater risk of dropping out. These findings align with Le et al. (2005), underscoring that the main source of variance in one’s psychosocial readiness is within-group and not between groups. Mapping every student’s college readiness profile would facilitate addressing each person’s specific needs in a tailored intervention. This approach highlights the importance of focusing on individuals' strengths and weaknesses (dispositional factors; Carroll et al., 2009; Bowles & Brindle, 2017) within the context of their group affiliation (situational factors) and college environment (institutional factors).

An examination of age-related differences showed that younger students scored higher on Academic Self-Efficacy, Educational Commitment, and Self-Discipline than older students but lower on Social Comfort and Resilience. Older students’ lower Academic Self-Efficacy may be due to the long gap since their most recent formal learning experience (Chung et al., 2017). Older students may also be mindful of the multiple roles and tasks they will have to cope with while in college and feel less confident about the prospects of success, contributing to their reported lower Self-Discipline. However, it may be that students who deferred college until after age 25 had been initially less confident about going to college due to self-doubts regarding their academic performance (Martin, 2010). In line with this explanation, whereas younger students report almost total commitment to college, older students’ responses tend to be more moderate (Gilardi & Guglielmetti, 2011).

Additionally, older-age students' higher resilience may reflect their emotional maturity stemming from cognitive development and life experience (Sorrel et al., 2016). Thus, older students’ previous coping experiences may have contributed to their resilience skills, such as emotion regulation and problem-solving (Chung et al., 2017). As hypothesized, older students scored higher on Social Comfort than younger participants, perhaps because of existing relationships with family and work colleagues outside of college. Thus, in light of their multiple life-role commitments off-campus, they have less time or interest in social engagement on campus (Rubin & Wright, 2017).

The findings showed that low-SES students and FGCS, representing less traditional college students, scored higher on Educational Commitment, reflecting their motivation to attain a college degree. They challenge the deficit conception positing that students from disadvantaged backgrounds lack critical traits or behaviors rather than highlighting their strengths (Garriott, 2020; Mckay & Devlin, 2016). These findings underscore that for students from disadvantaged backgrounds to get to the point of applying to college and persevere to graduation, they have to be driven by high levels of motivation (Mckay & Devlin, 2016; Sedlacek, 2011). It is beyond the scope of this paper to review the many antecedents, including structural and social barriers that motivate nontraditional young adults to struggle to attain a college education. Moreover, their challenges include facing inequality as they maneuver in higher education (Saar et al., 2014). These challenges notwithstanding, the present study points to a critical strength––their higher levels of educational commitment––as a resource that can help nontraditional students succeed in college.

The results of Study 3 supported the discriminant validity of the PRCq, reflected in the weak associations between scholastic aptitude measures and psychosocial factors, aligning with previous findings that differentiated between academic and psychosocial factors impacting college persistence (King & Ndum, 2017; Komarraju et al., 2013; Robbins et al., 2004). In Study 4, as expected, Big Five personality traits were associated with particular PRCq scales. These findings support the claim that the PRCq is associated with personality measures and is distinct from cognitive measures.

Finally, Study 5’s results support PRCq’s predictive validity. However, it is noteworthy that the observed support for the PRCq’s predictive validity is likely an underestimate of the detrimental effects of low psychosocial readiness on retention, and this is for two primary reasons. First, the dropout rate between the first and the second year among the participants who completed the PRCq at the specific research university was lower (5.8%) than the national dropout rate among first-year students (8.7%; CBS, 2018). Second, the participation rate for completing the PRCq was about 30% in the two studied cohorts. The dropout rate among those who did not comply with the request to complete the PRCq was higher (12.5%) than among the questionnaire completers (5.8%; Phillips-Berenstein & Gati, 2021). Thus, those agreeing to participate may have been more inclined to seek support and utilize relevant university resources when encountering difficulties.

Lower PRCq scores were associated with a higher risk of dropping out, and those revealing one or more risk factors were more likely to drop out than those with no risk factors. Moreover, the consequences of severe low readiness (i.e., scoring below the −2 SD cut-off) were dramatic: Whereas the dropout rate was 5% among those with no risk factors and 7% among those with a single risk factor, the dropout rate was 16% among those with very low readiness scores in two or more scales. Those with only a single risk factor may have more compensatory resources to cope with the various college challenges (You, 2018). Consequently, targeted interventions in urgency and intensity should prioritize students with two or more PRCq scale scores below the −2 SD cut-off. Future research should further explore each dimension’s differential effect and each scale’s optimal cut-off point. In addition, the PRCq’s contribution in predicting higher GPAs among the persisting students should also be examined as an additional criterion for academic success.

Limitations and Future Research

The research is the first of its kind and magnitude in Israel. However, the studies’ samples comprised less than 50% of the target population of first year students, as their participation in all institutions was voluntary and, thus, may not adequately represent all first-year students. Those agreeing to complete the readiness questionnaire are likely to be more motivated, committed, or socially and emotionally more confident than students who chose to ignore the invitation to complete a questionnaire aimed at “successful integration into college.” Given the desirability of higher participation rates, we suggest considering incentives to encourage completing the PRCq or having the academic institution make it mandatory after enrollment, before beginning college, or during orientation day.

Deferring college in Israel until age 25 is prevalent; however, an extended delay beyond age 25 can be attributed to the deliberation about whether to attend college or what to study. Future studies should consider exploring the reasons for this delay and its impact on persistence. Thus, the characteristics of the students in this research should be considered before drawing conclusions regarding age-equivalent students in other countries. Therefore, future research is necessary to determine the applicability of the PRCq for assessing psycho-social readiness to identify students at risk of dropping out in other countries.

Psychosocial college readiness facilitates college persistence and GPA (Allen et al., 2009; King & Ndum, 2017). Thus, future research should further test the predictive validity of the PRCq on first-year outcomes, over and above high school GPA and the PET. Predictive validity should be assessed for each dimension, and their respective contributions to each outcome should be examined. Finally, a critical challenge for further research and practice is identifying each scale’s optimal cut-off scores indicative of drop-out risk. As each scale yielded a different mean and standard deviation, future research should explore the utility of Z-scores or percentiles. Future studies should also test whether cut-off points differ for various groups (e.g., institutions, departments, gender, age, SES, or ethnic groups) and whether background and psychosocial factors have a cumulative effect on persistence.

Implications for Practice

The universality of the PRCq’s item content makes it suited to a wide range of young adults. These include students who begin their undergraduate studies after one or more gap years as well as students going straight into college. The PRCq is brief (30 items, 5–8 minutes), is administered online, and allows for immediate feedback.

Implication for Higher Education Institutions

The PRCq identifies students' unique patterns of readiness and hence, can facilitate designing tailored interventions for specific groups (e.g., part-time, evening, or international students), as well as for each student according to their unique needs and dispositional barriers. Elder (2021) recommended creating a comprehensive pre-college profile for each student (including background and non-cognitive variables) and being proactive in anticipating barriers. The PRCq can be a possible avenue in this direction. In addition to the direct beneficial effects of individual and group interventions on psychosocial factors (Fong et al., 2017), these interventions accord an important byproduct to academic institutions as a retention facilitator. Designing programs that nurture personal development increases feelings of belonging, especially among minority students (Atuahene, 2021). It remains for academic institutions to cultivate an advising culture that encourages students to use the available services (Mu & Fosnacht, 2019). While institutions cannot intervene when it comes to background variables such as age, gender, or FGCS, they can take measures to promote the social and academic integration of students from disadvantaged backgrounds (Atuahene, 2021), targeting areas emerging as potential hurdles to graduation by the PRCq.

The PRCq should not be used to screen for college admissions but rather as a means to augment student success following admission. Determining the optimal timing and setting for administering the PRCq is the key to successfully implementing early detection of potential college dropouts. Administering the questionnaire only after admission promotes candid replies and minimizes potential social desirability or self-concealing responses. Ideally, the PRCq should be routinely administered after the students commit to begin their studies (e.g., by paying the first tuition slip) but before the beginning of the school year to avail students of the support services that could facilitate their successful transition to college.

Implication for Counselors

Assessing factors of psychosocial readiness is crucial, as at-risk factors can be addressed through relatively brief and focused educational interventions (Fong et al., 2017; Gore et al., 2019), thus, offering the prospect of reducing risk factors and promoting college readiness at an early stage of college (Krumrei-Mancuso et al., 2013). The informative six-scale profile of the PRCq can be easily interpreted by professionals during the three stages of intervention: (a) identifying students at risk of dropping out, (b) tailoring the intervention to students’ specific needs, and (c) evaluating the effectiveness of the intervention. Flagging students with “low” readiness scores on one or more scales could be brought to the attention of college counselors to implement tailored interventions for individuals or for groups of students with similar needs.

Finally, PRCq’s interpretation should consider the relevant cohort and institutional baselines to determine the cut-off point for identifying at-risk students at each college. Therefore, when applying the PRCq to reduce dropout rates and design appropriate interventions, we suggest using standardized scale scores from the latest available cohort data. From a counseling perspective, there is a growing recognition that meeting students’ needs has become a critical feature of the counselor’s work, especially in the wake of growing student diversity (Chung et al., 2017), resulting in continued professionalization of college advising (Mu & Fosnacht, 2019).

Footnotes

Acknowledgments

We thank Ella Anghel, Benny A. Benjamin, Tair Buzaglo, Tony Gutentag, Nimrod Levin, Shada Kashkush, Yuliya Lipshits-Braziler, Michal Slama, and Adi Tene for their helpful comments on earlier versions of this paper.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The PRC questionnaire was and currently is free. It may be that in the future, the research and development authority of the Hebrew University will be interested in finding a partner to develop an elaborated feedback to each student and distribute it to all first year students in Israel.

Funding

This research was supported by the Israeli National Institute for Testing and Evaluation, The Rosalinde and Arthur Gilbert Foundation, and the Joseph S. and Caroline Gruss Life Monument Fund. Michal Phillips-Berenstein was partially supported by a Scholarship of The Seymour Fox School of Education, Tirza Willner was supported by the Azrieli Foundation. Itamar Gati was supported by the Samuel and Esther Melton Chair.

ORCID iDs

Michal Phillips-Berenstein

Tirza Willner

Itamar Gati

Appendix

Appendix A.

Exploratory Factor Analysis of the PRCq (University N = 1239)

	Factor
Item	1	2	3	4	5	6
ASE1	.87	.27	.33	.33	.36	.12
ASE2	.88	.25	.26	.31	.33	.15
ASE3	.92	.23	.39	.33	.40	.12
ASE4	.86	.22	.25	.29	.37	.20
ASE5	.89	.25	.34	.32	.42	.14
EC1	.26	.20	.87	.09	.26	−.08
EC2	.27	.20	.73	.15	.28	−.10
EC3	.28	.25	.89	.14	.29	−.14
EC4	.32	.25	.86	.17	.33	−.06
EC5	.34	.24	.76	.18	.39	−.11
SC1	.24	.24	.11	.72	.19	.19
SC2	.34	.50	.17	.78	.23	.17
SC3	.27	.29	.09	.79	.16	.19
SC4	.29	.36	.19	.83	.20	.30
SC5	.29	.36	.14	.84	.21	.25
CE1	.34	.78	.31	.39	.28	.01
CE2	.21	.89	.17	.42	.12	.00
CE3	.24	.91	.20	.39	.16	.00
CE4	.20	.84	.27	.24	.20	−.11
CE5	.26	.87	.25	.37	.24	.02
SD1r	.19	.08	.20	.08	.78	.18
SD2	.37	.18	.28	.18	.76	.04
SD3	.46	.26	.29	.33	.76	.08
SD4	.36	.20	.37	.26	.77	.08
SD5	.34	.20	.32	.20	.80	.02
RES1r	.10	−.06	−.08	.04	.13	.67
RES2r	.03	−.04	−.20	.23	−.12	.77
RES3r	.24	.07	.03	.28	.21	.74
RES4r	.15	.01	−.08	.27	.07	.83
RES5r	.14	.00	−.07	.24	.12	.71
% Variance Explained	26.94	12.07	10.63	6.66	6.15	5.43

Note. ASE = Academic Self-Efficacy, EC = Educational Commitment, SC = Social Comfort, CE = Campus Engagement, RES = Resiliency, SD = Self-Discipline; r-reversed item.

References

Ajayi

A. A.

Mitchell

L. L.

Nelson

S. C.

Fish

Peissig

L. H.

Causadias

J. M.

Syed

(2021). Person–environment fit and retention of racially minoritized college students: Recommendations for faculty, support staff, and administrators. Education Sciences, 11(6), 271–289. https://doi.org/10.3390/educsci11060271

Aljohani

(2016). A comprehensive review of the major studies and theoretical models of student retention in higher education. Higher Education Studies, 6(2), 1–18. https://doi.org/10.5539/hes.v6n2p

Allan

J. F.

McKenna

Dominey

(2014). Degrees of resilience: Profiling psychological resilience and prospective academic achievement in university inductees. British Journal of Guidance & Counselling, 42(1), 9–25. https://doi.org/10.1080/03069885.2013.793784

Allen

Robbins

S. B.

Sawyer

(2009). Can measuring psychosocial factors promote college success? Applied Measurement in Education, 23(1), 1–22. https://doi.org/10.1080/08957340903423503

Atuahene

(2021). Predicting the academic success of minority male students in a public 4-year institution in the USA. Journal of African American Studies, 25(1), 29-51. https://doi.org/10.1007/s12111-020-09512-4

Ayalon

Grodsky

Gamoran

Yogev

(2008). Diversification and inequality in higher education: A comparison of Israel and the United States. Sociology of Education, 81(3), 211–241. https://doi.org/10.1177/003804070808100301

Baars

G. J. A.

Arnold

I. J. M.

(2014). Early identification and characterization of students who drop out in the first year at university. Journal of College Student Retention: Research, Theory & Practice, 16(1), 95–109. https://doi.org/10.2190/CS.16.1.e

Bean

J. P.

(1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Res High Educ 12(1), 155–187. https://doi.org/10.1007/BF0097619

Bean

J. P.

(1983). The application of a model of turnover in work organizations to the student attrition process. The Review of Higher Education, 6(2), 129–148. https://doi.org/10.1353/rhe.1983.0026

10.

Biddix

J. P.

Gabourel

K. G.

Cuevas

(2021). Today's US College students by the numbers. In Gulley

N. Y.

(Ed), Multiple perspectives on college students: Needs, challenges and opportunities (pp. 12–32). Routledge.

11.

Bowles

T. V.

Brindle

K. A.

(2017). Identifying facilitating factors and barriers to improving student retention rates in tertiary teaching courses: A systematic review. Higher Education Research and Development, 36(5), 903–919. https://doi.org/10.1080/07294360.2016.1264927

12.

Bowman

N. A.

Hill

P. L.

Denson

Bronkema

(2015). Keep on truckin’or stay the course? Exploring grit dimensions as differential predictors of educational achievement, satisfaction, and intentions. Social Psychological and Personality Science, 6(6), 639–645. https://doi.org/10.1177/1948550615574300

13.

Brewer

H. E.

González-Espada

Boram

R. D.

(2021). Student retention in quantitative STEM majors: Science teachers and college students' perceptions of push and pull factors. Journal of the Kentucky Academy of Science, 82(1), 1–12. https://doi.org/10.3101/1098-7096-82.1.1

14.

Brock

(2010). Young adults and higher education: Barriers and breakthrouhgs to success. The Future of Children, 20(1), 109–132. https://doi.org/10.1353/foc.0.0040

15.

Brown

T. A.

(2006). Confirmatory factor analysis for applied research. Guilford Press.

16.

Burke

(2019). Student retention models in higher education: A literature review. College and University, 94(2), 12–21. https://www.proquest.com/scholarly-journals/student-retention-models-higher-education/docview/2232610556/se-2

17.

Burrus

Elliott

Brenneman

Markle

Carney

Moore

Betancourt

Jackson

Robbins

Kyllonen

Roberts

R. D.

(2013). Putting and keeping students on track: Toward a comprehensive model of college persistence and goal attainment. ETS Research Report Series, 2013(1), i61. https://doi.org/10.1002/j.2333-8504.2013.tb02321.x

18.

Carroll

Birch

(2009). Retention and progression of postgraduate business students: An Australian perspective. Open Learning, 24(3), 197–209. https://www.proquest.com/scholarly-journals/retention-progression-postgraduate-business/docview/61841657/se-2

19.

Casanova

J. R.

Gomes

C. M. A.

Bernardo

A. B.

Núñez

J. C.

Almeida

L. S.

(2021). Dimensionality and reliability of a screening instrument for students at-risk of dropping out from higher education. Studies in Educational Evaluation, 68, 100957. https://doi.org/10.1016/j.stueduc.2020.100957

20.

Central Bureau of Statistics (2017). First year students toward a degree in Universities. Academic Colleges and Colleges of Education Who Took the Psychometric Exams. (in Hebrew) https://www.cbs.gov.il/he/publications/DocLib/2017/8.ShnatonEducation/st08_49x.pdf

21.

Central Bureau of Statistics (2018). Drop-out after first year in higher education. CBS. (in Hebrew) https://www.cbs.gov.il/he/Events/DocLib/kenes/kns_2_45_20.pdf

22.

Central Bureau of Statistics (2019). Characterization and classification of statistical areas within municipalities and local councils by the socio-economic level of the population. CBS. https://www.cbs.gov.il/he/mediarelease/DocLib/2019/246/24_19_246b.pdf

23.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

24.

Choy

(2001). Students whose parents did not go to college: Postsecondary access, persistence, and attainment (NCES 2001-126). Department of Education, National Center for Education Statistics. http://nces.ed.gov/pubs2001/2001126.pdf

25.

Chung

Turnbull

Chur-Hansen

(2017). Differences in resilience between traditional and non-traditional university students. Active Learning in Higher Education, 18(1), 77–87. https://doi.org/10.1177/1469787417693493

26.

Collier

P. J.

Morgan

D. L.

(2008). Is that paper really due today?”: Differences in first-generation and traditional college students’ understandings of faculty expectations. Higher Education, 55(4), 425–446. https://doi.org/10.1007/s10734-007-9065-5

27.

Conger

Dickson

(2017). Gender imbalance in higher education: Insights for college administrators and researchers. Research in Higher Education, 58(2), 214–230. https://doi.org/10.1007/s11162-016-9421-3

28.

Costa

P. T.

Jr McCrae

R. R.

(1992). Four ways five factors are basic. Personality and Individual Differences, 13(6), 653–665. https://doi.org/10.1016/0191-8869(92)90236-I

29.

Davidson

O. B.

Feldman

D. B.

Margalit

(2011). Focused intervention for 1^st year college students: Promoting hope, sense of coherence, and self-efficacy. The Journal of Psychology: Interdisciplinary and Applied, 146(3), 333–352. https://doi.org/10.1080/00223980.2011.634862

30.

Elder

A. C.

(2021). Holistic factors related to student persistence at a large, public university. Journal of Further and Higher Education, 45(1), 65–78. https://doi.org/10.1080/0309877X.2020.1722802

31.

Elliott

D. C.

(2016). The impact of self beliefs on post-secondary transitions: The moderating effects of institutional selectivity. Higher Education, 71(3), 415–431. https://doi.org/10.1007/s10734-015-9913-7

32.

Fiske

S. T.

Markus

H. R.

(2012). A wide-angle lens on the psychology of social class. In Fiske

S. T.

Markus

H. R.

(Eds), Facing social class: Social psychology of social class (pp. 1–12). Russell Sage Foundation.

33.

Fong

C. J.

Davis

C. W.

Kim

Y. W.

Marriott

Kim

(2017). Psychosocial factors and community college student success: A meta-analytic investigation. Review of Educational Research, 87(2), 388–424. https://doi.org/10.3102/0034654316653479

34.

Garriott

P. O.

(2020). A critical cultural wealth model of first-generation and economically marginalized college students’ academic and career development. Journal of Career Development, 47(1), 80–95. https://doi.org/10.1177/0894845319826266

35.

Garriott

P. O.

Nisle

(2018). Stress, coping, and perceived academic goal progress in first-generation college students: The role of institutional supports. Journal of Diversity in Higher Education, 11(4), 436–450. https://doi.org/10.1037/dhe0000068

36.

Gilardi

Guglielmetti

(2011). University life of non-traditional students: Engagement styles and impact on attrition. The Journal of Higher Education, 82(1), 33–53. https://doi.org/10.1353/jhe.2011.0005

37.

Ginder

S. A.

Kelly-reid

J. E.

Mann

F. B.

(2017). Graduation rates for selected cohorts, 2008–13; outcome measures for cohort year 2008; student financial aid, academic year 2015– 16; and admissions in postsecondary institutions, fall 2016. National Center for Education Statistics. https://nces.ed.gov/pubs2017/2017150rev.pdf

38.

Gore

J. P. A.

Leuwerke

W. C.

Metz

A. J.

Brown

Kelly

A. R.

(2019). Measuring noncognitive factors related to college student outcomes: Development and initial construct validation of the student strengths inventory. Journal of Career Assessment, 27(1), 47–60. https://doi.org/10.1177/1069072717727463

39.

Grebennikov

Shah

(2012). Investigating attrition trends in order to improve student retention. Quality Assurance in Education, 20(3), 223–236. https://doi.org/10.1108/09684881211240295

40.

Guidi

(2018). Experiencing a gap year: Perceptions from students in the United States .(Doctoral dissertation). Northeastern University.

41.

Harackiewicz

J. M.

Canning

E. A.

Tibbetts

Priniski

S. J.

Hyde

J. S.

(2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class. Journal of Personality and Social Psychology, 111(5), 745–765. https://doi.org/10.1037/pspp0000075

42.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

43.

Huh

Huang

(2016). Examining the validity of ACT ® composite score and high school grade point average for predicting first-year college GPA of special-tested students. ACT research report series. https://www.act.org/content/dam/act/unsecured/documents/5931-research-report-2016-7-examining-the-validity-of-act-composite-score-and-hs-gpa.pdf

44.

John

O. P.

Donahue

E. M.

Kentle

R. L.

(1991). The Big five Inventory–versions 4a and 54. University of California, Institute of Personality and Social Research.

45.

John

O. P.

Srivastava

(1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In Pervin

L. A.

John

O. P.

(Eds), Handbook of personality (2nd ed., pp. 102–138). Guilford.

46.

Johnson

M. L.

Nussbaum

E. M.

(2012). Achievement goals and coping strategies: Identifying the traditional/nontraditional students who use them. Journal of College Student Development, 53(1), 41–54. https://doi.org/10.1353/csd.2012.0002

47.

Jury

Smeding

Stephens

N. M.

Nelson

J. E.

Aelenei

Darnon

(2017). The experience of low‐SES students in higher education: Psychological barriers to success and interventions to reduce social-class inequality. Journal of Social Issues, 73(1), 23–41. https://doi.org/10.1111/josi.12202

48.

Kahn

J. F.

Nauta

M. M.

Gailbreath

R. D.

Tipps

Chartrand

J. M.

(2002). The utility of career and personality assessment in predicting academic progress. Journal of Career Assessment, 10(1), 3–23. https://doi.org/10.1177/1069072702010001001

49.

Kim

Newton

F. B.

Downey

R. G. .

Benton

S. L.

(2010). Personal factors impacting college student success: Constructing college learning effectiveness inventory (CLEI). College Student Journal, 44(1), 1–18. https://search.proquest.com/docview/860370403?accountid=14546

50.

King

D. R.

Ndum

(2017). Can psychosocial factors predict first-to- second year college retention above and beyond standard variables? A mixed effects multinomial regression analysis. ACT. https://www.act.org/content/dam/act/unsecured/documents/R1656-psychosocial-factors-retention-2017-12.pdf

51.

Kleper

Turvall

(2016). A meta-analysis of the predictive validity of the Psychometric Entrance Test. National Institute for Testing & Evaluation. (in Hebrew) https://nite.org.il/files/reports/16_02.pdf

52.

Kline

R. B.

(2005). Principles and practice of structural equation modeling (2nd ed.). Guilford.

53.

Komarraju

Ramsey

Rinella

(2013). Cognitive and non-cognitive predictors of college readiness and performance: Role of academic discipline. Learning and Individual Differences, 24(1), 103–109. https://doi.org/10.1016/j.lindif.2012.12.007

54.

Kosonen

Puustinen

Tallberg

(2019). Saying no to military service—obligation, killing and inequality as experienced problems in conscription-based military in Finland. Journal of Military Studies, 8, 46–57. DOI:10.2478/jms-2019-0005.

55.

Krumrei-Mancuso

E. J.

Newton

F. B.

Kim

Wilcox

(2013). Psychosocial factors predicting first-year college student success. Journal of College Student Development, 54(3), 247–266. https://doi.org/10.1353/csd.2013.0034

56.

Lapan

R. T.

Turner

Pierce

M. E.

(2012). College and career readiness: Policy and research to support effective counseling in schools. In Fouad

N. A.

(Ed) APA handbook of counseling psychology. practice, interventions, and applications (Vol. 2, pp. 57–73). American Psychological Association.

57.

Laski

(2002). The effect of summer break activities and personality variables on stress and burnout among high school teachers in Israel. Unpublished thesis, Faculty of Management, Tel Aviv University.

58.

Casillas

Robbins

Langley

(2005). Motivational and skills, social, and self-management predictors of college outcomes: Constructing the student readiness inventory. Educational and Psychological Measurement, 65(3), 482–508. https://doi.org/10.1177/0013164404272493

59.

Lipshits-Braziler

Gati

Tatar

(2015). Strategies for coping with career indecision: Concurrent and predictive validity. Journal of Vocational Behavior, 91(1), 170–179. https://doi.org/10.1016/j.jvb.2015.10.004

60.

Lombardi

Seburn

Conley

(2011). Development and initial validation of a measure of academic behaviors associated with college and career readiness. Journal of Career Assessment, 19(4), 375–391. https://doi.org/10.1177/1069072711409345

61.

Lufi

Parish-Plass

Cohen

(2003). Persistence in higher education and its relationship to other personality variables. College Student Journal, 37(1), 50–59. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9469990&site=eds-live

62.

Pender

Welch

(2016). Education pays 2016: The benefits of higher education for individuals and society. In Trends in higher education series . The College Board. https://files.eric.ed.gov/fulltext/ED572548.pdf

63.

Markle

(2015). Factors influencing persistence among nontraditional university students. Adult Education Quarterly, 65(3), 267–285. https://doi.org/10.1177/0741713615583085

64.

Martin

A. J.

(2010). Should students have a gap year? Motivation and performance factors relevant to time out after completing school. Journal of Educational Psychology, 102(3), 561–576. https://doi.org/10.1037/a0019321

65.

Martin

A. J.

Wilson

Liem

G. A. D.

Ginns

(2013). Academic momentum at university/college: Exploring the roles of prior learning, life experience, and ongoing performance in academic achievement across time. The Journal of Higher Education, 84(5), 640–674. https://doi.org/10.1353/jhe.2013.0029

66.

Mckay

Devlin

(2016). ‘Low income doesn't mean stupid and destined for failure’: Challenging the deficit discourse around students from low SES backgrounds in higher education. International Journal of Inclusive Education, 20(4), 1–17. https://doi.org/10.1080/13603116.2015.1079273

67.

Fosnacht

(2019). Effective advising: How academic advising influences student learning outcomes in different institutional contexts. The Review of Higher Education, 42(4), 1283–1307. https://doi.org/10.1353/rhe.2019.0066

68.

National Center for Education Statistics . (2022). Undergraduate retention and graduation rates. Condition of Education. U.S. Department of Education, Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator/ctr

69.

OECD. (2021). Education at a glance 2021: OECD indicators. OECD Publishing. https://doi.org/10.1787/b35a14e5-en

70.

OECD. (2022). Education at a glance 2022: OECD indicators. OECD Publishing. https://doi.org/10.1787/3197152b-en

71.

Pascarella

E. T.

Terenzini

P. T.

(2005). How college affects students: A third decade of research. San Francisco, CA: Jossey-Bass.

72.

Peterson

C. H.

Casillas

Robbins

S. B.

(2006). The student readiness inventory and the Big five: Examining social desirability and college academic performance. Personality and Individual Differences, 41(4), 663–673. https://doi.org/10.1016/j.paid.2006.03.006

73.

Phillips-Berenstein

Gati

(2021). Decreasing drop-out from college by early detection of students at risk using the psychosocial readiness for college questionnaire. People and Work, Israeli Journal of Career Development, 17, 22–34. (In Hebrew).

74.

Radunzel

Noble

(2012). Tracking 2003 ACT-tested high school graduates: College readiness, enrollment, and long-term success. ACT. https://files.eric.ed.gov/fulltext/ED542012.pdf

75.

Richardson

Abraham

Bond

(2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838

76.

Robbins

S. B.

Allen

Casillas

Peterson

C. H.

(2006). Unraveling the differential effects of motivational and skills, social, and self-management measures from traditional predictors of college outcomes. Journal of Educational Psychology, 98(3), 598–616. https://doi.org/10.1037/0022-0663.98.3.598

77.

Robbins

S. B.

Lauver

Davis

Langley

Carlstrom

(2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130(2), 261–288. https://doi.org/10.1037/0033-2909.130.2.261

78.

Rubin

Wright

C. L.

(2017). Time and money explain social class differences in students' social integration at university. Studies in Higher Education, 42(2), 315–330. https://doi.org/10.1080/03075079.2015.1045481

79.

Saar

Täht

Roosalu

(2014). Institutional barriers for adults’ participation in higher education in thirteen European countries. Higher Education: The International Journal of Higher Education and Educational Planning, 68(5), 691–710. https://doi.org/10.1007/s10734-014-9739-8

80.

Sedlacek

W. E.

(2011). Using noncognitive variables in assessing readiness for higher education. Readings on Equal Education, 25(13), 187–205. http://web.augsburg.edu/em/UsingNCV-Sedlacek.pdf

81.

Shwed

Shavit

(2006). Occupational and economic attainments of college and university graduates in Israel. European Sociological Review, 22(4), 431–442. https://doi.org/10.1093/esr/jcl006

82.

Sorrel

M. A.

Fernández-Pinto

Extremera

Fernández-berrocal

(2016). Age and gender differences in ability emotional intelligence in adults: A cross-sectional study. Developmental Psychology, 52(9), 1486–1492. https://doi.org/10.1037/dev0000191

83.

Tieben

(2020). Non-completion, transfer, and dropout of traditional and non-traditional students in Germany. Research in Higher Education, 61, 117–141. https://doi.org/10.1007/s11162-019-09553-z

84.

Tinto

(1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.2307/1170024

85.

Tinto

(1997). Classrooms as communities: Exploring the educational character of student persistence. The Journal of Higher Education, 68(6), 599–623. https://doi.org/10.2307/2959965

86.

Van Rhijn

T. M.

Lero

D. S.

Bridge

Fritz

V. A.

(2016). Unmet needs: Challenges to success from the perspectives of mature university students. The Canadian Journal for the Study of Adult Education, 28(1), 29–47. https://search.proquest.com/docview/2101886250?accountid=14546

87.

You

J. W.

(2018). Testing the three-way interaction effect of academic stress, academic self-efficacy, and task value on persistence in learning among Korean college students. Higher Education, 76(5), 921–935. https://doi.org/10.1007/s10734-018-0255-0