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META-ANALYSIS OF PRE-HIRE EXPERIENCE 1
A Meta-Analysis of the Criterion-related Validity of Pre-hire Work Experience
Chad H. Van Iddekinge
John D. Arnold
Florida State University
Rachel E. Frieder
University of North Florida
Philip L. Roth
Clemson University
Author Note:
An earlier version of this article was presented at the 77th Annual Meeting of the Academy of
Management, Chicago, IL. We are grateful to the many primary study authors who provided us
data, results, or other information for use in the meta-analysis.
META-ANALYSIS OF PRE-HIRE EXPERIENCE 2
Abstract
Organizations frequently screen or select job applicants based on their work experience. Despite
this, surprisingly little is known about the criterion-related validity of pre-hire experience, which
reflects the amount, duration, or type of experience workers have acquired before they enter a
new organization. To address this critical gap in the literature, we used meta-analysis to
synthesize data from 81 independent samples that reported relations between pre-hire experience
and performance or turnover. Results revealed overall corrected correlations of .06 for job
performance (k = 43, n = 11,577), .11 for training performance (k = 21, n = 8,176), and .00 for
turnover (k = 32, n = 11,676). Measures that capture pre-hire experience with tasks, jobs, or
occupations relevant to workers’ current position also are only weakly related to the outcomes
(e.g., = .07 for job performance). Two exceptions to our main findings are that (a) pre-hire
experience is somewhat more predictive of job performance when workers first start a new job
and (b) measures of task-level experience predict training performance, although these results are
based on small subsets of primary studies. Overall, the present findings suggest that the types of
pre-hire experience measures organizations currently use to screen job applicants generally are
poor predictors of future performance and turnover. We therefore caution organizations from
selecting employees based on such measures unless more positive evidence emerges.
Keywords: experience, staffing, performance, turnover, meta-analysis
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 3
A Meta-Analysis of the Criterion-related Validity of Pre-hire Work Experience
A glance at any online job board will attest to the importance organizations place on
work experience when recruiting and selecting job applicants. For example, we reviewed 115
monster.com job ads and found that 82% of jobs either required or preferred experience (see
Chakrabarti [2018] and Modestino, Shoag, and Ballance [2015] for similar results). Even jobs
that were described as “entry-level” often required experience.1 In fact, research suggests that
experience is one of the most widely used methods by which organizations assess job applicants
(e.g., Wilk & Cappelli, 2003). Furthermore, staffing professionals tend to (a) infer knowledge,
skills, and abilities from applicants’ experience (e.g., Brown & Campion, 1994), (b) weigh
experience heavily when assessing applicants (e.g., Singer & Bruhns, 1991), and (c) prefer
applicants who possess experience (e.g., Rynes, Orlitzky, & Bretz, 1997).
Given the emphasis organizations tend to place on experience during the selection
process, we were surprised by how little attention pre-hire experience—which reflects
experience workers have acquired before they enter a new organization—has received in the
staffing literature. Instead, research has tended to focus on how post-hire experience—which
reflects experience workers have acquired in their current organization—relates to performance
in that organization (Dokko, Wilk, & Rothbard, 2009). For example, Quińones, Ford, and
Teachout (1995) meta-analyzed the results of 22 studies that related experience (measured
primarily by job or organizational tenure) to job performance and found a mean observed
correlation (̅) of .22 and a mean corrected correlation ( ) of .27. Similarly, Sturman (2003)
1 We used the 23 job families from the Occupational Information Network (O*NET) to develop a keyword for each
job family. We then reviewed the first five job ads returned for each family (N =115). Further information about our
review of job ads is available from the first author upon request.
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meta-analyzed relations between experience (measured primarily in terms of job tenure) and
found an ̅ of .12 and a of .13.2 Sturman also found that experience was more strongly related
to production records than to supervisor ratings ( = .28 vs .15) and was more strongly related
to performance in high-complexity jobs than in low-complexity jobs ( = .20 vs .09). Other
research has focused on relations between total experience—which reflects experience workers
have acquired in both prior and current organizations—and job performance. For instance,
McDaniel, Schmidt, and Hunter (1988a) conducted a meta-analysis of the General Aptitude Test
Battery (GATB) database and found an ̅ of .21 and a of .32 between employees’ total
experience and supervisor ratings of performance.
Thus, research suggests that experience workers acquire in their current organization, or
their total work experience in both prior and current organizations, relates positively to current
job performance. However, we suggest that pre-hire experience differs from post-hire and total
experience in several important ways. First, post-hire experience reflects what workers have
done in their current organization. In contrast, pre-hire experience reflects workers’ experiences
in previous organizations, which may or may not be relevant to their current job. Second, post-
hire experience tends to be proximal to performance in one’s current job, whereas pre-hire
experience is more distal to current performance, and in some cases, may have been acquired
many years in the past. Third, post-hire and total experience cannot be used as a predictor in
most selection contexts because external applicants are not currently employed by the
2 Sturman’s (2003) study was somewhat different from the Quinones et al. (1995) meta-analyses given its focus on
testing nonlinear relations between experience, tenure, age, and performance. For example, Sturman included only
experience measures that were expressed in units of time and focused on job-level experience (p. 619). Similarly,
meta-analyses by Ng and Feldman (2010, 2013) focused on relations between job or organizational tenure and
performance in the same job/organization.
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 5
organization. As such, research that has examined relations between post-hire or total work
experience and outcomes does not address whether applicants’ experience in prior organizations
is a valid predictor of future job performance or turnover.
In sum, very little is known about the criterion-related validity of pre-hire experience,
which appears to be one of the most frequently used and heavily weighted variables for
evaluating job applicants. The present study begins to address this critical gap by synthesizing
data from primary studies that report relations between measures of pre-hire experience and
performance or turnover. In doing so, this study makes several important contributions. First, the
typical assumption in the literature, as well as in practice, seems to be that pre-hire experience is
beneficial, and even essential for some jobs. For example, researchers have suggested that
experience is a good predictor of what workers will do in the future (e.g., Arvey, McGowan, &
Horgan, 1981), can be a proxy for factors such as job knowledge (e.g., Schmitt, Cortina,
Ingerick, & Wiechmann, 2003), and may be an indication of commitment to a particular
occupation (e.g., Porter, Levine, & Flory, 1976). However, we present theories and arguments
that suggest that experience applicants acquired in prior jobs and organizations might not be a
good predictor of performance in a different organization. We also contribute to theory by
identifying and testing several potential boundary conditions of relations between pre-hire
experience and performance. For instance, we examine the relative validity of different
operationalizations of pre-hire experience (e.g., quantity vs. quality of experience, specific vs.
general experience), as well as whether relations between pre-hire experience and job
performance change over time.
Second, previous meta-analyses have focused on relations between post-hire or total
work experience and job performance and have not cumulated evidence on whether or how
META-ANALYSIS OF PRE-HIRE EXPERIENCE 6
experience relates to other important outcomes. For example, although researchers have
proposed that experience may facilitate learning during training (e.g., Tesluk & Jacobs, 1998),
prior meta-analyses have not considered relations between experience and training performance.
This is unfortunate given that performance during training is a key outcome for many
organizations (e.g., military and law enforcement jobs that require new hires to pass long-term
training) and is an acceptable criterion for validation studies (see section 14B3 of the Uniform
Guidelines, 1978). Relations between experience and withdrawal behaviors (e.g., turnover) also
have received very little research attention. Because turnover is a concern for many
organizations, it is important to know whether pre-hire experience predicts future withdrawal.
We provide what appear to be the first meta-analytic data on whether and how pre-hire
experience relates to these outcomes.
Finally, from a practical standpoint, many organizations have been selecting job
applicants on pre-hire experience with limited understanding of its criterion-related validity. The
present findings will help organizations determine the extent to which such screens are indeed
effective. The results of our meta-analysis also will identify situations in which pre-hire
experience may be most useful for predicting future performance or turnover.
Theoretical Perspectives, Hypotheses, and Research Questions
Defining Pre-hire Experience
Before we discuss our hypotheses and research questions, we define pre-hire experience
and distinguish it from other constructs. Building on existing definitions of work experience
(e.g., Quińones et al., 1995), we define pre-hire experience as the amount, duration, or type of
work experience workers have acquired prior to entering a new organization. Examples of pre-
hire experience measures include years of work experience (i.e., duration of experience), whether
META-ANALYSIS OF PRE-HIRE EXPERIENCE 7
applicants possess experience in the type of job to which they are applying (i.e., amount of
experience), and the similarity of tasks applicants performed in their prior job compared to the
job to which they are applying (i.e., type of experience). This definition aligns with the types of
pre-hire experience measures organizations appear to be using to screen job applicants. For
example, our review of online job ads revealed four main types of pre-hire experience
organizations require or prefer: (a) years of general work experience, (b) the possession (yes or
no) of relevant experience, (c) years of relevant experience, and (d) the possession of task-
specific experience.
It is important to note how pre-hire experience differs from related constructs. As
discussed, pre-hire experience is different than post-hire experience, which reflects experiences
workers have acquired in their current organization. Measures such as job and organizational
tenure capture this type of experience (e.g., Ng & Feldman, 2010; Sturman, 2003). Research on
leader development also tends to focus on post-hire experiences (e.g., Dragoni, Oh, Vankatwyk,
& Tesluk, 2011). Seibert, Sargent, Kraimer, and Kiazad (2017) identified three main types of
developmental experiences, which include formal programs such as on-the-job training, job
challenges such as dealing with employee problems, and supervision in the way of coaching and
mentoring. Although it is important to understand the experiences leaders and other employees
gain in their current organization, measures of post-hire experience cannot be used to assess
applicants who are not yet in the organization.
Pre-hire experience also differs from past performance, which focuses on how well
workers performed in past situations. Examples of selection procedures that attempt to assess
past performance include behavioral interviews (Janz, 1982) and structured reference checks
(Taylor, Pajo, Cheung, & Stringfield, 2004). For instance, behavioral interviews elicit
META-ANALYSIS OF PRE-HIRE EXPERIENCE 8
information about how applicants behaved in past job-relevant situations, and interviewers
evaluate the effectiveness of that behavior. In contrast, pre-hire experience measures focus on the
possession of certain experiences and not on the effectiveness of behavior workers demonstrated
during those experiences.
Pre-Hire Experience and Performance
Having defined pre-hire experience and distinguished it from other constructs, we now
discuss whether and how pre-hire experience may relate to future performance or turnover.
Several theoretical perspectives suggest that experience performing a task or job is positively
related to subsequent performance on that task/job. For instance, theory and research on skill
acquisition suggests the existence of a learning curve, such that task performance improves as
people gain experience (and, in turn, knowledge and skills) with the task (e.g., Ackerman, 1987;
Ackerman, Kanfer, & Goff, 1995). Similarly, the Schmidt-Hunter model of job performance
(Schmidt & Hunter, 1992; Schmidt, Hunter, & Outerbridge, 1986) proposes that experience
provides workers an opportunity to learn job-relevant knowledge and skills that, in turn, enable
them to perform their jobs more effectively (see also Borman, Hanson, Oppler, Pulakos, &
White, 1993).
However, these theories—and tests of these theories—tend to focus on experience (or
learning) and performance of the same task or job over time. In contrast, we are interested in
whether experience in prior organizations affects how workers perform in a different
organization. Several theories and arguments suggest that experience may be job- or
organization-specific and not a strong predictor of performance in a different job/organization.
First, tasks—and the knowledge, skills, abilities, and other characteristics (KSAOs) needed to
perform those tasks—can vary greatly between jobs. Even the same job in different organizations
META-ANALYSIS OF PRE-HIRE EXPERIENCE 9
often involves somewhat different tasks or requires slightly different attributes (Dokko et al.,
2009). As such, experiences workers acquire in a job in one organization might not transfer to
performance of a different job in a new organization.
Second, several theories suggest that experience in one organization may not help—and
even may impede—performance in a different organization. Human capital theory (Becker,
1964; Ployhart, Nyberg, Reilly, & Maltarich, 2014) proposes that units and firms possess two
main types of human capital that emerge from the characteristics (e.g., experience, education,
skills) of employees within a unit or firm (e.g., Nyberg, Moliterno, Hale, & Lepak, 2014;
Ployhart & Moliterno, 2011). General human capital represents proficiencies that are relevant to
various jobs and organizations and, thus, is transferable from one organization to another. The
aggregate general mental ability among unit or firm employees would be an example of general
human capital. In contrast, firm-specific human capital is relevant only to the particular
organization where it is developed and cannot be transferred if individuals change organizations.
Employees’ collective knowledge of an organization’s products and services would be a firm-
specific form of human capital.
We suggest that pre-hire experience may reflect a specific rather than a general form of
human capital and, thus, not always transfer across organizations. For example, experience
employees acquire with firm-specific routines and processes may not be relevant to other
organizations whose routines and processes are different. Specific human capital also includes
social networks and relationships that help workers perform their jobs more efficiently (Bolino,
Turnley, & Bloodgood, 2002; Nahapiet, & Ghoshal, 1998). However, these social networks tend
to be organization-specific and, thus, are unlikely to help workers when they assume a new job in
a different organization.
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Similarly, theory on cognitive schemas suggests that scripts or habits developed in one
context do not always help, and may even hinder, performance in another context (Woltz,
Gardner, & Bell, 2000). Cognitive schemas are frameworks developed from experience that help
people make sense of events and guide their behavior (Dokko et al., 2009; Gioia & Poole, 1984).
Schemas developed in one context can facilitate performance in that context, such as by
simplifying performance of repeated tasks. However, schemas tend to be context-specific and not
relevant to performance in a different context. Thus, schemas workers develop from pre-hire
experience might not help them perform in a new organization that possesses different structures,
systems, and norms (Dokko et al., 2009; Groysberg, Lee, & Nanda, 2008).
Finally, as discussed, pre-hire experience reflects the amount, duration, or type of
experience workers have accumulated prior to entering a new organization. Measures of pre-hire
experience do not directly assess KSAOs applicants may have acquired from these experiences
or the effectiveness of their behavior in those situations, both of which are central to theoretical
models of job performance (e.g., Campbell, 1990). Similarly, measures of pre-hire experience do
not tend to capture what workers may have learned from those experiences or the extent to which
they reflected upon those experiences, which research suggests is critical to learning (DeRue,
Nahrgang, Hollenbeck, & Workman, 2012).
In sum, jobs often comprise a somewhat unique set of tasks and a particular combination
of KSAOs needed to perform those tasks. As such, experience in one job may not help workers
perform another job that involves different tasks and/or KSAOs. Additionally, human capital
theory and theories on transfer of learning and cognitive schemas suggest that experience may be
firm-specific and not generalize to performance in a different organization. For example, pre-hire
experience may interfere with subsequent performance if the schemas and habits workers
META-ANALYSIS OF PRE-HIRE EXPERIENCE 11
developed in prior organizations differ from those needed to perform successfully in another
organization. Furthermore, possession of pre-hire experience is not the same as KSAOs
applicants gained from that experience or their level of prior performance. These perspectives,
therefore, suggest that pre-hire experience will not be a strong predictor of future performance.
This leads to our first hypothesis:
Hypothesis 1: Pre-hire experience will be a weak predictor of future performance.
Potential Moderators of Relations between Pre-Hire Experience and Performance
Although the theoretical perspectives and arguments discussed above seem to suggest
that pre-hire experience will not be a strong predictor of performance, we explore several
situations in which such experience may be more likely to predict future performance.
Job versus training performance. As noted, previous meta-analyses have focused on
how post-hire or total experience relates to job performance and have not examined whether or
how experience in general—or pre-hire experience in particular—predicts other outcomes,
including training performance. The theoretical basis for a relation between pre-hire experience
and performance during training may be similar to the perspectives for a relation between pre-
hire experience and job performance described above. For example, schemas workers developed
in previous organizations may impede their ability to learn a new job if the organization’s norms
and procedures differ from those in workers’ previous organizations.
However, differences between job and training contexts could make pre-hire experience a
somewhat better predictor of training performance. For one, experience is thought to provide
workers opportunities to develop knowledge and skills (e.g., Schmidt et al., 1986), which also is
the main focus of most training programs. Indeed, training often is more focused on developing
task-oriented knowledge than on developing non-task knowledge (e.g., organizational
META-ANALYSIS OF PRE-HIRE EXPERIENCE 12
procedures) for which pre-hire experience may not be as useful. In addition, training often is
used to help new workers learn the job. In these cases, pre-hire experience may be relatively
proximal in time to the training and, thus, may exert a somewhat stronger influence on training
performance than on subsequent job performance. These arguments lead to the following
hypothesis:
Hypothesis 2: Pre-hire experience will be more strongly related to training performance
than to job performance.
Relevant versus general pre-hire experience. One distinction among pre-hire
experience measures involves measures that assess experience relevant to a job to which
someone is applying versus measures that assess experience in general. As an example,
applicants for a customer service representative position could be asked whether they have prior
customer service-related experience (i.e., relevant experience), or they could be asked about their
overall work experience (i.e., general experience).
Transfer of learning theory (Ellis, 1965; Thorndike & Woodworth, 1901) suggests that
transfer of learning occurs when experience on a task influences performance on some
subsequent task. Further, transfer of learning is thought to be stronger when a prior task and a
current task are similar and weaker when they are different (Gentner, Rattermann, & Forbus,
1993). As such, prior jobs that involved similar tasks and KSAOs may help workers perform
better in a subsequent job that involves similar tasks/KSAOs. In contrast, general work
experience—which may include experience in both similar and different jobs to the one
applicants are applying—may be less useful to subsequent performance. This suggests that
relations between pre-hire experience and performance may be somewhat stronger for measures
that assess relevant experience and weaker for measures that assess general work experience.
META-ANALYSIS OF PRE-HIRE EXPERIENCE 13
Hypothesis 3: Relations between pre-hire experience and performance will be stronger
for measures that assess relevant experience than for measures that assess general
experience.
Mode and level of pre-hire experience measures. Quińones et al. (1995) introduced a
model that categorizes work experience measures according to two dimensions. Measurement
mode describes how experience is assessed and categorizes measures as to whether they reflect
amount of experience (e.g., number of prior jobs), time of experience (e.g., years of experience),
or type of experience (e.g., experience with tasks similar to one’s current job tasks). Similarly,
Tesluk and James (1998) distinguished between quantitative versus qualitative experience
measures. Quantitative measures assess the amount or duration of experience, whereas
qualitative measures assess the types or quality of experience. An example of a qualitative
measure would be asking applicants or interviewers to evaluate the similarity between the tasks
applicants performed in their prior job and the tasks the job to which they are applying requires.
Qualitative measures sometimes attempt to capture the type of pre-hire experience, such
as the breadth, complexity, or challenges associated with job tasks and responsibilities (Quińones
et al., 1995; Tesluk & James, 1998). Such measures also may assess the relatedness of pre-hire
experience, such as the similarity between prior jobs and the job to which applicants are being
considered (Dokko et al., 2009). In contrast, quantitative measures, such as amount or duration
of pre-hire experience, would seem less likely to capture the nature or job-relevance of that
experience. For example, two applicants could possess the same number of years of experience,
but the types of experiences they possess could vary greatly in terms of their relevance to the job.
We therefore predict that experience-performance relations will be stronger for measures that
META-ANALYSIS OF PRE-HIRE EXPERIENCE 14
reflect qualitative aspects of pre-hire experience than for measures that reflect the mere quantity
of experience.
Hypothesis 4: Relations between pre-hire experience and performance will be stronger
for qualitative experience measures than for quantitative measures.
The second dimension in Quińones et al.’s (1995) model is level of specificity, which
categorizes measures as to whether they reflect task-, job-, or organization-level experience. For
example, number of prior jobs would be a job-level measure, whereas number of prior employers
would be an organization-level measure. Tesluk and James (1998) added work group- and
occupation-levels of experience, and we add two more levels based on the types of experience
measures we found in the literature. First, industry-level experience reflects experience in a
particular industry, such as banking or hospitality. Second, measures of general experience (e.g.,
years of work experience) do not specify a particular level of experience and likely encompass
various types and levels of experience.
We predict that experience-performance relations will be stronger among measures that
assess more specific levels of pre-hire experience (i.e., task and job levels) and weaker among
measures that assess more general levels of experience (i.e., occupation, organization, industry,
and general levels). The basis for this hypothesis is that performance measures often focus on
task- or job-level attributes or behaviors. As such, matching the bandwidth of the predictor (i.e.,
pre-hire task or job experience) and the criterion (i.e., task or overall job performance) may
increase their relations (Cronbach & Gleser, 1965; Hogan & Holland, 2003).
Hypothesis 5: Relations between pre-hire experience and performance will be stronger
when experience is measured at more specific levels and weaker when it is measured at
more general levels.
META-ANALYSIS OF PRE-HIRE EXPERIENCE 15
Relations between pre-hire experience and performance over time. If pre-hire
experience affects performance, it may be most likely to do so when workers first enter a job.
Theory and research on new employee socialization suggests that experience may help workers
socialize more quickly into their new role (Beus, Jarrett, Taylor, & Wiese, 2014; Beyer &
Hannah, 2002; Fisher, 1986). For example, experience may help workers make sense of their
new environment and minimize information processing requirements so they can focus on
learning the job (Louis, 1980). Similarly, uncertainty reduction is a necessary goal for new
employees (Zahrly & Tosi, 1989). New employees with pre-hire experience may face less
uncertainty and, thus, be able to devote more time to learning the job and organizational norms.
In contrast, newcomers who possess little or no pre-hire experience may face considerable
uncertainty and take longer to adjust to their new roles. However, these advantages may diminish
as workers without pre-hire experience gain exposure to the new job and organization.
Hypothesis 6: Relations between pre-hire experience and performance will be strongest
when workers first enter a new job but weaken over time as workers gain experience in
the job.
Job complexity. We also explore whether the criterion-related validity of pre-hire
experience depends on the job. One factor on which jobs vary is their complexity, which
concerns the cognitive demands jobs place on employees (McDaniel et al., 1988a). Previous
research that examined whether cognitive demands influence the criterion-related validity of
work experience on performance has found divergent results. McDaniel et al., for instance, found
that total experience (i.e., pre- and post-hire experience) was more strongly related to
performance in low-complexity jobs than in high-complexity jobs ( = .39 vs. .28). The
researchers suggested that workers in high-complexity jobs have multiple avenues by which they
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can acquire the knowledge and skills needed to perform their jobs, including formal education
and on-the-job experience. In contrast, experience may be the primary means by which workers
in low-complexity jobs acquire the necessary knowledge and skills. Sturman (2003), on the other
hand, found that experience (i.e., primarily post-hire experience) was more strongly related to
performance in high-complexity jobs than in low-complexity jobs ( = .20 vs. .09).
We extend this prior research by examining whether job complexity moderates relations
between pre-hire experience and performance. Given the lack of a strong theoretical basis or a
consistent empirical precedent, we pose the following research question:
Research Question 1: Does job complexity moderate the criterion-related validity of pre-
hire experience?
Dimension of performance. Finally, we examine whether relations between pre-hire
experience and performance vary by performance dimension. Theories that predict relations
between experience and performance propose that experience performing particular tasks should
help workers perform those tasks in the future (e.g., Ackerman, 1987; Schmidt et al., 1986). This
suggests pre-hire experience is most likely to influence future task performance.
It is perhaps less clear whether or how pre-hire experience may relate to overall job
performance, which, in addition to task performance, includes organizational citizenship
behavior (OCB) and counterproductive work behavior (CWB; Rotundo & Sackett, 2002). On the
one hand, pre-hire experience may be a weaker predictor of performance measures that capture
non-task behaviors. For example, more discretionary types of behaviors such as OCB and CWB
often are predicted by personality traits (e.g., Mount, Ilies, & Johnson, 2006) and organizational
factors (e.g., LePine, Erez, & Johnson, 2002) rather than by variables such as experience and job
knowledge. On the other hand, perhaps workers who possess pre-hire experience need to devote
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 17
less effort to learning or performing job tasks and, therefore, have greater capacity to go above
and beyond the basic responsibilities, such as by engaging in OCB (e.g., helping coworkers). Or,
perhaps workers who possess certain types of pre-hire experiences (e.g., working for many
different companies) tend to engage in CWB (e.g., absenteeism) that lead to turnover. Given the
lack of a strong theoretical basis for such possibilities, we explore the following question:
Research Question 2: Is the criterion-related validity of pre-hire experience stronger for
task performance than for overall job performance?
Pre-hire Experience and Turnover
Previous meta-analyses have not considered whether or how experience predicts
withdrawal behaviors such as turnover intentions and actual turnover. Several theories would
seem to predict that pre-hire experience will be negatively related to turnover. For example,
theory and research on realistic job previews (e.g., Wanous, 1978) suggests that workers who
possess pre-hire experience may have more realistic expectations about jobs and organizations
than workers without such experience. These realistic expectations, in turn, may help workers
with pre-hire experience adjust to undesirable, but expected, aspects of their new job or
organization, whereas workers without pre-hire experience may deal with the undesirable aspects
by leaving (Beyer & Hannah, 2002; Carr, Pearson, Vest, & Boyar, 2006; Meglino, DeNisi, &
Ravlin, 1993). Similarly, the attraction-similarity-attrition (ASA) model (Schneider, 1987) would
predict that experienced workers apply for jobs they think will be a good fit for them. For
instance, workers who possess pre-hire experience may better understand the types of jobs and
organizations that match their interests, values, and KSAOs and, in turn, stay longer in those
positions than workers with more limited prior experience (Kristof‐Brown, Zimmerman, &
Johnson, 2005; Schneider, 1987).
META-ANALYSIS OF PRE-HIRE EXPERIENCE 18
Measurement mode may be important when considering possible relations between pre-
hire experience and turnover. For example, researchers sometimes measure pre-hire experience
in terms of how long applicants worked for their previous organization (e.g., McClelland &
Rhodes, 1969; Weekley & Ployhart, 2005). Given that theory on behavioral consistency suggests
that past behavior tends to predict future behavior (Janz, 1982; Wernimont & Campbell, 1968),
workers who remained with their previous organization(s) for a long period also may stay longer
in future jobs (Judge & Watanabe, 1995). In contrast, pre-hire experience measures that assess
number of prior organizations might indicate “job-hopping” and be positively related to
subsequent turnover. This would be consistent with the “hobo syndrome” (Ghiselli, 1974), which
suggests that some workers tend to move from job to job regardless of conditions or available
alternatives (Judge & Watanabe, 1995; Woo, 2011).
In sum, although theoretical arguments exist to suggest that pre-hire experience should be
associated with lower turnover, the nature of pre-hire experience may play a role. As such, we
pose the following question:
Research Question 3: Does pre-hire experience predict future turnover intentions or
actual turnover?
Method
Literature Search
We began by searching for primary studies included in previous work experience meta-
analyses (Quińones et al., 1995; Sturman, 2003).3 As discussed, these meta-analyses tended to
focus on post-hire or total experience and included no or very few studies that measured pre-hire
3 McDaniel et al.’s (1988a) meta-analysis included only concurrent validity studies from the GATB research
program. In addition, all studies measured total work experience (i.e., “How much experience have you had in your
present occupation? Include time with both your present and previous employers”, p. 328). Thus, we did not
incorporate any of the data from this meta-analysis into our study.
META-ANALYSIS OF PRE-HIRE EXPERIENCE 19
experience. Thus, we had to expand our search for relevant studies. Another challenge was that
experience often serves as a descriptive variable or control rather than as a focal variable. As
such, we could not rely on scanning study titles and abstracts but had to search for keywords
within the text of studies themselves.
We searched various online and electronic databases, including ABI/INFORM
Collection, Education Resources Information Center (ERIC), Google Scholar, Proquest
Dissertations & Theses, PsycINFO, and Web of Science. We also searched the metaBUS
database (Bosco, Steel, Oswald, Uggerslev, & Field, 2015). In addition, because studies in the
labor economics literature often examine or control for some form of work experience, we
conducted a separate search of journals in this area, including American Economic Review,
Econometrica, Journal of Political Economy, Labour Economics, Journal of Labor Economics,
and Journal of Human Resources.
We used many combinations of key terms in an attempt to be as comprehensive as
possible. Search terms for experience included task, job, work, occupation, industry, and career
experience. We also searched studies that included other types of assessments that sometimes
measure experience-related variables, including training and experience (T&E) measures,
accomplishment records, and biodata inventories. Search terms for performance included
contextual performance, counterproductive work behavior (and CWB), deviance, effectiveness,
employee errors, extra-role, instructor ratings, organizational citizenship behavior (and OCB),
performance, productivity, sales, supervisor ratings, and training. Search terms for turnover
included attrition, movement out of the organization, quit, retention, stay, and turnover. We also
paired some of these terms with intentions and cognititions to find studies that measured turnover
intentions rather than actual turnover. Finally, we reviewed the references sections of the studies
META-ANALYSIS OF PRE-HIRE EXPERIENCE 20
we obtained to identify additional sources. Ultimately, our searches yielded approximately 1,700
studies we reviewed for potential inclusion in the meta-analysis.
Inclusion Criteria
The target population included (a) individuals working in formal jobs or (b) individuals
training for a formal job. With this population in mind, we used six criteria to determine whether
to include primary studies in the meta-analysis. First, we only included studies that measured
experience workers gained prior to their current organization. We excluded studies in which the
predictor reflected post-hire experience (e.g., Ferris & Rowland, 1987) or total experience (e.g.,
Motowidllo & Van Scotter, 1994). We also excluded a few studies that measured experience in a
different job within the same organization (e.g., Bettin & Kennedy, 1991), which is different
from our focus on experience in different organizations. Further, we excluded studies for which
it was not completely clear whether a measure reflected pre-hire experience or something
different (e.g., Spiker, Harper, & Hayes, 1985). However, before excluding such studies, we tried
to contact the original authors for clarification. Further, some studies used age as a proxy for
experience (e.g., Lamont & Lundstrom, 1977), but we did not include these studies. We only
included measures that directly assessed the amount, duration, or type of pre-hire experience.
Second, we only included studies that measured (a) job performance, (b) training
performance, or (c) turnover. We excluded studies in which the criterion reflected academic
performance (e.g., Krausz, Schiff, Schiff, & VanHise, 1999) or performance on an experimental
task (e.g., Orvis, Horn, & Belanich, 2008). Further, we only included studies that measured job
performance using supervisor or peer ratings or productivity records (e.g., sales), whereas we
excluded self-ratings of performance (e.g., Claiborne, 2002). The one exception is that,
consistent with previous meta-analyses (e.g., Gonzalez-Mulé, Mount, & Oh, 2014), we included
META-ANALYSIS OF PRE-HIRE EXPERIENCE 21
self-reports of CWB. We also included studies in which work samples were used as performance
criteria. However, we excluded work sample tests that applicants completed as part of the
selection process (e.g., Rodrigues & Rebelo, 2009). We also excluded studies that measured
variables such as promotions and salary (e.g., McCarty, 1957) that may be the result of
performance but also other factors (e.g., tenure). For training performance, we included studies
that measured performance using exam scores, grades, or instructor ratings. For turnover, we
included studies that measure turnover intentions or actual turnover. In addition, we included
both voluntary and involuntary forms of turnover, as well as studies that focused on overall
turnover.
Third, we only included studies that measured outcomes at the individual level. We
excluded studies that measured outcomes at other levels of analysis, such as team- or firm-level
performance (e.g., Hmieleski & Baron, 2009). Fourth, we only included studies that did not
appear to artificially enhance the range of pre-hire experience within the sample. For instance,
we excluded studies that used extreme-group designs that compared highly experienced versus
inexperienced workers and excluded workers with average amounts of experience (e.g., Lie,
1998). Fifth, we only included studies that reported point estimates for relationships between all
relevant measures of experience and the outcome(s) included in the study. We excluded studies
that reported correlations or other effect sizes that were statistically significant but not those that
were nonsignificant (e.g., Cotham, 1969). Finally, we only included independent samples.
We found 118 studies that appeared to meet all the criteria. However, in many cases, the
original authors did not report relations between pre-hire experience and the outcome(s). For
example, some studies reported the percentage of the sample that possessed pre-hire experience,
but did not include this variable in the correlation table. Therefore, we attempted to contact many
META-ANALYSIS OF PRE-HIRE EXPERIENCE 22
of the original authors to request the relevant results. Ultimately, we obtained data for 75 studies,
including 82 independent samples. These studies comprised 53 journal articles, 17 dissertations
and theses, four technical reports, and one unpublished manuscript. Only eight of the 75 studies
were included in previous experience meta-analyses (all in Sturman, 2003) and, thus, almost
90% of the data are unique to the present meta-analysis. The primary studies included a range of
jobs and organizations, including 15 of the 23 job families in the O*NET taxonomy. The most
prevalent jobs included protective service (22.5%, such as police officers and correction
officers), transportation and material moving jobs (16.9%, such as pilots and truck drivers), and
sales related (15.5%, such as salespeople and customer service representatives).
Coding of Primary Studies
Two or three of the authors independently coded each primary study and then met to
discuss the codes. We coded several aspects of the pre-hire experience measure(s) in each study.
We coded measurement mode as amount, duration, or type of experience. We coded level of
experience as task, job, occupation, industry, or general. In addition, we coded whether the
measure reflected relevant or general experience. Examples of relevant experience include years
of pre-hire experience in jobs similar to the one employees currently hold and self-report ratings
of the similarity between employees’ previous job and their current job.
To measure job complexity, we first identified primary studies in our dataset that (a)
focused on one job (we excluded multi-job studies from this analysis) and (b) clearly described
the job (we excluded studies for which we could not be confident about the job). We then
matched each job to the most relevant job in the O*NET database (intercoder agreement = 98%).
Next, consistent with prior research (e.g., Dierdorff, Rubin, & Morgeson, 2009; Shaffer &
Postlethwaite, 2013), for each job we computed the sum of scores based on O*NET analyst
META-ANALYSIS OF PRE-HIRE EXPERIENCE 23
ratings of the 21 cognitive abilities in the O*NET taxonomy. These abilities represent seven
higher-order categories, which include verbal ability, idea generation and reasoning abilities,
quantitative ability, memory, perceptual ability, spatial ability, and attentiveness. We used the
level of ability each occupation requires, which analysts rated on a 0-7 scale with behavioral
examples for low, moderate, and high levels specific to each ability. The internal consistency
reliability for the sum of these scores was .97.
The percentage of inter-coder agreement for our initial codes of measurement mode,
level, and relevance were 91.7%, 89.6%, and 99.1%, respectively. Agreement indices for some
of the other key variables were as follows: 93.7% for sample sizes, 97.1% for outcome reliability
estimates, and 95.2% for experience-outcome correlations. The Appendix provides the final
codes and values for each independent sample.
Meta-analytic Approach
We used Schmidt and Hunter’s (2015) psychometric meta-analysis to analyze the data.
First, we computed the mean, sample size-weighted observed correlation (r) between pre-hire
experience and each outcome. Although most primary studies reported zero-order rs, a few
studies reported other statistics, such as means and standard deviations (e.g., for groups of
participants with and without pre-hire experience). In these instances, we converted the reported
statistics to rs. Also, for studies that included multiple pre-hire experience measures, we used the
measure that appeared most relevant to the current job in the overall meta-analysis. For example,
we used a measure of whether applicants had experience in a job like the one to which they were
applying over a measure of the number of prior jobs held.
Second, we computed rs corrected for measurement error in the outcome to estimate the
operational validity of pre-hire experience. For studies that reported an estimate of criterion
META-ANALYSIS OF PRE-HIRE EXPERIENCE 24
reliability, we used that value in the meta-analyses. For studies that did not report a reliability
estimate, we used the median estimate based on other studies in the data set.
Four studies reported an interrater reliability estimate for supervisor ratings of job
performance. The median single- and k-rater reliabilities across these studies were .60 and .79,
respectively. The single-rater reliability of .60 is the same as, or slightly higher than, values
found in prior meta-analyses (e.g., Rothstein, 1990; Van Iddekinge, Roth, Putka, & Lanivich,
2011; Viswesvaran, Ones, & Schmidt, 1996). Four studies reported a reliability estimate for
productivity records. The median test-retest reliability was .72, which is similar to previous
meta-analytic estimates (e.g., .77; Van Iddekinge et al., 2011). None of the primary studies
reported a reliability estimate for work samples. However, we obtained some work sample
estimates from another researcher (Jesus F. Salgado), and the median internal consistency
reliability (alpha) across 19 samples was .80. One study used self-reports of CWB, and the alpha
reliability was .76.
Three studies reported information we could use to estimate the reliability of training
exams, and the median reliability was .79. This value is very similar to values around .80 used in
prior meta-analyses (e.g., Van Iddekinge, Aguinis, Mackey, & DeOrtentiis, 2018; Van
Iddekinge, Roth, Raymark, & Odle-Dusseau, 2012). We used this reliability estimate for studies
that measured performance with scores on training tests or simulations. We also used this
estimate for a few studies in which the criterion reflected whether participants passed training.
None of the primary studies reported a reliability estimate for instructor ratings of training
performance. Thus, we used the interrater reliability of .56 for training success ratings from
Salgado et al. (2003).
META-ANALYSIS OF PRE-HIRE EXPERIENCE 25
Eight studies reported internal consistency reliability estimates for turnover intentions,
and the median estimate was .83. Consistent with prior research (e.g., Griffeth, Hom, &
Gaertner, 2000; Harrison, Newman, & Roth, 2006; Van Iddekinge et al., 2011), we did not
correct for measurement error in turnover, which is assumed to be measured with minimal error.
However, we did correct correlations between pre-hire experience and turnover for departure
from a 50-50 split between leavers and stayers (Kemery, Dunlap, & Griffeth, 1988; Zimmerman,
2008). This correction controls for differences in turnover base rate across studies, which if not
corrected, could falsely indicate the existence of moderators (Hunter & Schmidt, 2004). The
median turnover base rate across primary studies was .24. We used this value for one study that
did not report the turnover base rate.
Finally, we attempted to determine the likelihood and nature of range restriction within
each primary study (Berry, Sackett, & Landers, 2007; Van Iddekinge et al., 2012). For most
primary studies (approximately 80%), it was unclear whether participants originally were
selected on pre-hire experience. For example, many studies measured pre-hire experience for
research purposes (e.g., to describe the sample) and did not discuss whether or how experience
was considered when employees initially were selected. In other studies, the authors indicated
that participants were not selected on pre-hire experience. Finally, in a few studies, it appeared
likely that participants were selected on the basis of pre-hire experience, typically along with one
or more other predictors.
Only three studies reported the standard deviation of pre-hire experience in both the
research sample and the original applicant sample. Using these data, we computed a range
restriction ratio (u) 1.14 (median value across four experience measures) from Castilla (2002), a
u of 1.02 from Dokko et al. (2009), and a u of .99 (mean across two experience measures) from
META-ANALYSIS OF PRE-HIRE EXPERIENCE 26
Matyas (1980). The median u across the three studies (1.05) suggests there was as much
variance, and in some instances slightly more variance, in pre-hire experience within the study
samples as there was within the original applicant pools. Given the small number of values on
which the distribution of u values would be based, as well as the absence of range restriction in
the few primary studies that provided such information, we decided not to correct for range
restriction in our main analyses. However, in the Discussion section we present several
illustrative corrections that test the robustness of our findings.
Results
In the tables, we report results for analyses for which there were at least two primary
studies. Also, we report results with and without influential cases if excluding a primary study or
studies changed by .05 or more.
Relations between Pre-hire Experience and Performance
Overall relations. Table 1 presents the meta-analytic results for relations between pre-
hire experience and job performance, and Table 2 presents results for training performance.
Hypothesis 1 predicted that pre-hire experience would be weakly related to performance. Across
44 independent samples (N = 11,785), ̅ for relations between pre-hire experience and job
performance was .05 and was .06. The 80% credibility interval (CV) of extended from -.11
to .22, which indicates that a number of estimated population correlations were negative. For
relations between pre-hire experience and training performance, ̅ was .09 and was .11 (k =
21, N = 8,176). Similar to job performance, the CV of extended down to -.08, suggesting a
number of population correlations were negative. Overall, these results provide support for
Hypothesis 1 and suggest that pre-hire experience is a weak predictor of future performance.
ρ
ˆ
ρ
ˆ
ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 27
Moderators of relations between pre-hire experience and performance. Job versus
training performance. Hypothesis 2 predicted that pre-hire experience would be a better
predictor of training performance than of job performance. Relations between pre-hire
experience and training performance were indeed somewhat larger than relations between
experience and job performance (see Tables 1 and 2). However, the 95% confidence interval (CI)
for job performance ( = .01 to .10) overlapped with the CI for training performance ( = .04
to .18). Thus, these results provide little support for Hypothesis 2.
Relevant versus general pre-hire experience. Hypothesis 3 predicted that relations
between pre-hire experience and performance would be stronger for measures that capture
experience relevant to workers current job than for measures that reflect general experience. For
job performance, was .07 for relevant experience and -.02 for general experience (which
increased to .03 when we excluded an outlier, see Table 1). The CIs around these estimates
overlapped (.03 to .12 vs. -.10 to .06).4 Thus, these results do not provide much support for
Hypothesis 3.
Mode and level of pre-hire experience measures. Hypothesis 4 predicted that relations
between pre-hire experience and performance would be stronger for qualitative experience
measures than for quantitative measures. Unfortunately, we found very few studies that
measured qualitative aspects of pre-hire experience. Among three studies that related type of pre-
hire experience with job performance, was .01, which was comparable to, or slightly lower
4 Because all but one training performance study measured relevant pre-hire experience, we could not compare
validity estimates of relevant versus general experience within these studies.
ρ
ˆ
ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 28
than, values of .03 and .05 for quantitative measures of amount and time of experience,
respectively.5 Thus, Hypothesis 4 was not supported.
Hypothesis 5 predicted that experience-performance relations would be stronger when
pre-hire experience is measured at more specific levels (e.g., task level) and weaker when
measured at more general levels (e.g., organization level). For job performance (see Table 1),
was -.01 for task-level experience, .09 for job-level experience, .04 for organization-level
experience, .05 for occupation-level experience, .06 for industry-level experience (which
decreased to -.04 when we excluded an outlier), and -.17 for general-level experience (which
decreased to -.04 when we excluded an outlier). For training performance (see Table 2), was
.32 for task-level experience, .00 for job-level experience, and .06 for occupation-level
experience. These results do not tend to support Hypothesis 5. The one exception was that task-
level experience was a notably better predictor of training performance than was job- and
occupation-level experience.
Time on the job. Hypothesis 6 predicted that relations between pre-hire experience and
job performance would be strongest when workers first begin a new job and then weaken over
time. We tested this hypothesis in two ways. First, we coded the time that had elapsed between
organizational entry and when supervisors rated employees’ performance. This information was
available for 22 primary studies. We then conducted a weighted least squares (WLS) multiple
regression analysis (Steel & Kammeyer-Mueller, 2002) with employee tenure as the independent
variable and rs between pre-hire experience and job performance as the dependent variable. We
weighted each study by the inverse of the sampling error variance, such that studies with less
5 No training performance studies included qualitative measures of pre-hire experience.
ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 29
sampling error received greater weight than studies with more sampling error. Results suggested
that as time in the organization increased, the criterion-related validity of pre-hire experience
decreased (β = -.33, p = .13). However, removing one influential case reduced the standardized
regression coefficient to -.06 (p = .80).
Second, seven primary studies reported rs between pre-hire experience and job
performance at multiple periods. Among four studies (n = 1,226) that measured relations
between experience and performance after 1-3 months on the job, was .22. Among six studies
(n = 1,769) that measured these relations after 6-12 months on the job, was .08. And among
four studies (n = 796) that measured these relations after one year or longer on the job, was
.05. In addition, two studies (n = 211) measured relations between pre-hire experience and
training performance at two times. Estimates of for time 1 and time 2 were .33 and .06,
respectively. Overall, results of the WLS regression and analysis of time-varying studies provide
partial support for Hypothesis 6.
Job complexity. Research Question 1 asked whether job complexity would moderate
relations between pre-hire experience and performance. To help answer this question, we
performed a WLS regression analysis with job complexity as the independent variable and
experience-performance rs as the dependent variable. The sample included 51 independent
samples that measured job or training performance. We first examined the linear relation
between job complexity and experience-performance rs and found a standardized regression
coefficient of .13 (p = .38).
In addition, previous meta-analyses have found that the effects of job complexity on
relations between cognitive ability and variables such as job performance and ethnicity often are
ρ
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ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 30
nonlinear (e.g., Berry, Clark, & McClure, 2011; Roth, BeVier, Bobko, Switzer, & Tyler, 2001).
We therefore examined the possibility of a nonlinear relationship between job complexity and
experience-performance rs. Specifically, we added a quadratic job complexity term to the model
with the linear term, which resulted in a significant change in variance explained (change in
adjusted R2 = .18, p < .001). The standardized regression coefficient for the quadratic term was
.45 (p < .001). To interpret the nature of this effect, we graphed the rs, which revealed a U-
shaped relationship. That is, pre-hire experience-performance relations tended to be stronger
when job complexity was relatively low or high and weaker when job complexity was moderate.
We also conducted separate meta-analyses for subgroups of low, moderate, and highly complex
jobs (see Table 1). The pattern of results was consistent with the finding that experience-
performance relations were strongest at the extremes of job complexity.
Performance dimension. Research Question 2 asked whether the criterion-related
validity of pre-hire experience would be stronger for task performance than for overall job
performance. Although was about two times larger for task performance (.13) than for overall
job performance (.06), both estimates were small and their CIs overlapped substantially (.05 to
.16 vs. .02 to .10). In addition, two studies included a separate measure of OCB for which was
.15, and four studies included a separate measure of CWB for which was -.11 (which
decreased to -.02 when we excluded an outlier). Overall, the validity of pre-hire experience did
not vary substantially by performance dimension. However, most of these results are based on
small numbers of primary studies and, thus, should be considered preliminary.
Finally, we also report results based on (a) the nature of the performance measures and
(b) whether studies were published or unpublished. Primary studies measured job performance
ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 31
using supervisor ratings, productivity records, or work sample tests. The resulting values were
.08, .05, and .08, respectively. For training performance, although was slightly larger for
training exams (.14) than for ratings of training performance (.06), the CIs around these two
estimates overlapped (-.04 to .16 vs. .03 to .26). Thus, type of performance measure did not
appear to exert a strong influence on relations between pre-hire experience and performance.
Regarding publication status, among primary studies that measured job performance, was .07
for published studies and -.01 for unpublished studies. Once again, the CIs for these two
estimates overlapped (.02 to .12 vs. -.07 to .03). Further, was .11 for both published and
unpublished studies that measured training performance. These results suggest that the criterion-
related validity of pre-hire experience did not vary greatly depending on whether studies were
published or unpublished.
Relations between Pre-hire Experience and Turnover
Research Question 3 asked whether pre-hire experience would be related to turnover.
Table 3 displays the pertinent results. We (re)coded all the values such that positive rs indicate
that more experience is related to lower turnover intentions and actual turnover. Both, ̅ and
were .00 (k = 32, N = 11,676). Although the percentage of variance due to artifacts was only
22.30%, the CV of -.13 to .13 suggests that relations between pre-hire experience and turnover
were weak in most situations and even negative in a number of cases.
We also examined whether relations between pre-hire experience and turnover varied by
the nature of the experience or turnover measure. Regarding relevant versus general pre-hire
experience, was .03 for relevant experience and .00 for general experience. There also were
only small validity differences according to measurement mode ( = -.02 to .05) and level ( =
ρ
ˆ
ρ
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ρ
ˆ
ρ
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ρ
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ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 32
-.07 to .07). In addition, pre-hire experience was unrelated to turnover intentions ( = -.00) and
actual turnover ( = .00), although removing two outliers increased the relationship with
turnover intentions to .08. Finally, pre-hire experience was unrelated to voluntary and
involuntary turnover ( = .03 and .01, respectively) and in both published and unpublished
studies ( = -.00 and .01, respectively). Thus, the answer to Research Question 3 appears to be
that pre-hire experience is not a good predictor of future turnover.
Discussion
Organizations frequently assess pre-hire work experience during the selection process,
such as by requiring a certain number of years of experience as a minimum qualification. The
popularity of evaluating pre-hire experience probably is due to its conceptual appeal, low cost,
and ease of administration (McGonigle & Curnow, 2007). Yet, surprisingly little is known about
the criterion-related validity of this widely used predictor. Instead, most research has focused on
whether and how employees’ post-hire or total work experience relates to performance in their
current job. The present study addressed this critical gap in the staffing literature by synthesizing
evidence from primary studies that examined relations between pre-hire experience and
outcomes organizations may wish to predict when assessing job applicants.
Summary of Key Findings
The meta-analytic results provide only weak support for the criterion-related validity of
pre-hire experience. Overall values are .06 for job performance, .11 for training performance,
and .00 for turnover. In fact, only a few mean corrected validity estimates exceeded .10. Even
pre-hire experience measures that would seem most promising tend to be only weakly related to
outcomes. For example, pre-hire experience in the same or similar jobs correlates only .07 with
ρ
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ρ
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ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 33
future job performance. In addition, researchers have suggested that measures that focus more on
the quality of experience, rather than on the amount or duration of experience, may better predict
outcomes (e.g., Tesluk & Jacobs, 1998). The present findings provide evidence that measures
that assess the perceived relevance of pre-hire experience also correlate weakly with outcomes
(e.g., = .01 for job performance).
Thus, the present findings suggest that pre-hire experience tends to be a weak predictor of
future performance, including both job and training performance. Pre-hire experience also is very
weakly related to future withdrawal, including both voluntary and involuntary turnover. The fact
that these relations are weaker than those found in previous research (e.g., Quińones et al., 1995;
McDaniel et al., 1988a) is consistent with the notion that pre-hire experience is fundamentally
different than post-hire and total work experience. As such, researchers and practitioners should
be careful when drawing conclusions about the validity or usefulness of “work experience,”
which may differ depending on whether experience involves pre-hire, post-hire, or total
experience.
However, moderator analyses revealed a couple of situations in which pre-hire
experience could be somewhat useful, although sample sizes often were small. First, relations
between pre-hire experience and job performance are somewhat stronger when workers first start
a new job ( = .22) and become weaker as workers accumulate experience in the job ( = .05
to .08). This suggests that pre-hire experience may be most valuable when workers first enter a
job but becomes less relevant over time. Second, pre-hire experience measures that assess the
number of times performing a task or set of tasks tend to predict training performance ( = .32).
This finding is based primarily on studies that examined prior flying or driving experience as a
predictor of training performance. A possible explanation for this finding is that the predictor
ρ
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ρ
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ρ
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ρ
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 34
(e.g., hours of prior flying experience) and criterion (e.g., flying performance during pilot
training) are better matched for this particular analysis than they are for most other types of pre-
hire experience and criteria. This would be consistent with the finding that matching the
bandwidth of predictor and criterion variables can increase their relations (Cronbach, & Gleser,
1965).
Furthermore, recall that only a few primary studies provided information concerning
range restriction, and that all of these studies suggested a lack of restriction. However, here we
consider what would happen if there was range restriction within some of the other primary
studies in our data set. We examined this possibility using three possible levels of range
restriction (u): .75, .85, and .95. These values reflect moderate to minimal amounts of restriction
and are in line with u values for other predictors, such as cognitive ability and personality
measures (e.g., Schmidt, Shaffer, & Oh, 2008). We then corrected the mean observed rs between
pre-hire experience and the outcomes for indirect range restriction (Hunter, Schmidt, & Le,
2006). The resulting estimates account for range restriction in pre-hire experience and
measurement error in performance or deviations from a 50% base rate in turnover.
Table 4 displays the results of these analyses. Given the small relations between pre-hire
experience and the outcomes, the range restricted-corrected values also tend to be small. For
example, corrected correlations between experience and job performance varied between .07 and
.09 across the range restriction ratios. Thus, our main results did not change after correcting for
possible range restriction. The only corrected values that exceeded .15 were those for early job
performance (i.e., after 1-3 months on the job), which ranged from .25 to .32. However, recall
that these latter validity estimates are based on small subsets of primary studies. Overall, these
analyses suggest that even if there was a moderate amount of range restriction, pre-hire
META-ANALYSIS OF PRE-HIRE EXPERIENCE 35
experience tends to be only weakly correlated with performance and turnover. Once again, these
results are illustrative and need to be interpreted with caution given the limited primary study
information concerning range restriction.
Implications for Theory and Practice
A key implication of the present results is that extant measures of experience workers
acquire in one organization tend to be poor predictors of performance or retention in subsequent
organizations. This finding has implications for human capital theory and research, which has
tended to consider pre-hire experience a form of general human capital (Becker, 1964; Crook,
Todd, Combs, Woehr, & Ketchen, 2011). Instead, our results suggest that pre-hire experience
may be a firm-specific form of human capital that holds little value outside of the organization in
which it was developed.
Furthermore, the present results do not appear to support theoretical perspectives that
suggest that pre-hire experience enables workers to develop knowledge and skills that will help
them perform in subsequent jobs (e.g., Schmidt et al., 1986). Results also were not consistent
with the idea that workers with higher levels of pre-hire experience possess more accurate
expectations of future jobs and, in turn, demonstrate better performance or longer retention (e.g.,
Beyer & Hannah, 2002). Instead, results seem to be more in line with theory on negative transfer
of learning, which predicts that what workers learn from experience in prior jobs and
organizations does not necessarily transfer to subsequent jobs that involve different tasks and
KSAOs, or to organizations that possess different cultures, norms, and systems (Salomon &
Perkins, 1989). Some of our findings also appear to be consistent with socialization theories
(e.g., Fisher, 1986; Louis, 1980) that would suggest that pre-hire experience helps new workers
META-ANALYSIS OF PRE-HIRE EXPERIENCE 36
socialize and learn their jobs more quickly. Nonetheless, the positive effects of pre-hire
experience on performance seem to dissipate over time as workers gain experience on the job.
The findings also have clear implications for practice. Importantly, results suggest that
organizations may be screening out applicants who do not possess the desired amount or type of
pre-hire experience, yet possibly could perform well on the job. Thus, organizations who screen
out applicants with minimal experience unnecessarily may be reducing the pool of qualified
applicants. Given this, we encourage organizations to reconsider their use of existing measures
of pre-hire experience until more promising evidence emerges.
If organizations are going to select on pre-hire experience, our findings suggest that the
best chance to observe some level of criterion-related validity might be for jobs for which initial
performance is particularly important. This is based on the fact that validities are somewhat
larger (though still quite small) for early job performance and for some training performance
situations. For example, pre-hire experience could hold some promise for predicting performance
in short-term, temporary jobs. Even then, organizations should consider pre-hire experience vis-
à-vis predictors with better track records of criterion-related validity, such as academic
performance (Roth, BeVier, Switzer, & Schippmann, 1996), situational judgment tests
(McDaniel, Morgeson, Finnegan, Campion, & Braverman, 2001), and structured interviews
(Huffcutt & Arthur, 1994). However, not requiring pre-hire experience could affect the types of
procedures organizations can use to assess applicants. For example, job knowledge tests and
certain types of work samples might not be appropriate for assessing applicants who lack pre-
hire experience in the target job (Schmidt & Hunter, 1998). Similarly, applicants without pre-hire
experience might have difficulty answering structured interview questions that ask them how
they have behaved in job-specific situations.
META-ANALYSIS OF PRE-HIRE EXPERIENCE 37
Limitations and Directions for Future Research
We note a couple of potential limitations of our research, as well as some ideas for how
future research might address these limitations and other important unanswered questions. First,
we synthesized data from a relatively large number of independent samples that measured pre-
hire experience, the vast majority of which had not been included in prior meta-analyses. At the
same time, the number of studies available for some outcomes, and for many of the sub-analyses,
was small. Thus, conclusions based on these analyses are more tentative.
Second, as discussed, only a few primary studies provided information concerning the
amount or type (e.g., direct vs. indirect) of range restriction on measures of pre-hire experience.
Although the illustrative analyses we conducted suggested that correcting for possible range
restriction would not substantively affect our results or change conclusions, we still encourage
future primary studies to report range restriction information whenever possible.
The present results suggest that existing measures of pre-hire experience have limited
criterion-related validity. Future studies might attempt to assess what construct(s) pre-hire
experience measures actually capture. This type of research might shed further light on the small
relations we observed between such measures and outcomes. During the review process, we
explored relations between pre-hire experience and other predictors reported in the primary
studies we synthesized. Table 5 reports observed rs for these relations, as well as rs corrected for
measurement error in the other predictors. Pre-hire experience was most strongly related to age
( = .25). This relationship varied somewhat based on how experience was measured, such that
was larger for time-based measures of pre-hire experience (.35) than for amount- and type-
based measures (.24, and .06, respectively). Results also revealed modest relations between pre-
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 38
hire experience and job knowledge ( = .19)6 and to a lesser extent with measures of person-
environment fit ( = .10 to .13). Relations between pre-hire experience and cognitive ability,
personality, and self-efficacy were even smaller ( = .01 to .08). Most of these results are based
on small numbers of primary studies, and we hope future research can further examine the
constructs pre-hire experience measures may capture.
A key need for future research is to explore alternative measures that may hold greater
predictive value. For example, early research suggested that T&E rating methods—whereby
raters evaluate applicants’ experience based on materials such as applications and resumes—may
hold some promise (e.g., McDaniel, Schmidt, & Hunter, 1988b). This research indicated that
assessments that focus on past performance or job knowledge were better predictors of
performance than assessments that focus on experience in particular jobs. Thus, future research
might explore ways to collect and evaluate these types of information. Similarly, future research
might explore whether the more specific types of experiences on which employee development
research often focuses could be used when evaluating applicants’ pre-hire experiences. As an
example, applicants for managerial positions could be asked about their experience creating
change, motivating others, and managing budgets, as well as what they learned from those
experiences. Nonetheless, even measures such as these may have somewhat limited predictive
value given their focus on the possession of pre-hire experience rather than on the level of
performance in those experiences.
6 Four of the six primary studies underlying this estimate used situational judgment tests, which often are thought to
capture job-specific and/or general procedural knowledge (e.g., Motowidlo & Beier, 2010). Excluding these studies
would reduce from .19 to .10 (k = 2, n = 266).
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 39
We found some evidence to suggest that pre-hire experience may be somewhat more
useful when workers first enter a new organization. An alternative explanation of the decreasing
correlations over time is that the variance in performance becomes more restricted as some
employees in the original sample are promoted (e.g., due to high performance) or turn over (e.g.,
due to low performance or dissatisfaction). Future primary studies could take a closer look at
these and other possibilities. For example, time-series studies could track the effects of pre-
versus post-hire experience on employee behaviors. Findings of such studies could help
organizations determine whether the positive effects of pre-hire experience last long enough to
justify using this variable for selection. These studies also might attempt to identify the point in
new employees’ tenure when post-hire experiences (e.g., in terms of training, job rotation) may
become more important than their pre-hire experiences.
We were interested in whether experiences workers acquire in prior organizations predict
performance or turnover in a subsequent organization. Future research could examine whether
experience employees acquire in their present job predicts their performance in a different job
within the same organization. Answering this question might shed additional light on the present
results. For example, experience in prior jobs within the same organization may be a good
predictor of performance in a different job within that organization. If so, this may suggest that
the weak validity evidence we observed is due to differences between organizations (e.g., in
terms of culture or routines) rather than to differences between jobs. If this is the case, perhaps
experience is more useful for assessing internal job candidates (e.g., for promotions) than for
assessing external candidates.
Finally, there appears to be almost no research concerning whether the use of pre-hire
experience for decision-making is associated with subgroup differences or produces differential
META-ANALYSIS OF PRE-HIRE EXPERIENCE 40
prediction. Given the prevalence with which organizations use pre-hire experience for selection,
as well as the current evidence that commonly used measures of pre-hire experience largely are
unrelated to performance and turnover, it is critical to understand how selecting on pre-hire
experience affects issues related to adverse impact and organizational diversity. For example,
perhaps members of traditionally underrepresented workers (e.g., females, ethnic minorities)
have fewer opportunities to gain experience than other groups of workers and, in turn, are
selected at lower rates when pre-hire experience is assessed.
Conclusion
The present study addressed a critical gap in the staffing literature regarding the criterion-
related validity of experience workers have acquired prior to entering a new organization.
Although organizations often require applicants to possess pre-hire experience, our findings
suggest that commonly used measures of such experience largely are unrelated to performance
and turnover in subsequent organizations. As such, we suggest that organizations refrain from
using pre-hire experience to screen job applicants unless they possess evidence that this variable
predicts valued outcomes and does not produce adverse impact. We also call for research that
attempts to identify better ways to assess pre-hire experience and the job-relevant attributes and
behaviors applicants developed from those experiences.
META-ANALYSIS OF PRE-HIRE EXPERIENCE 41
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META-ANALYSIS OF PRE-HIRE EXPERIENCE 54
Table 1
Meta-analytic Results for Pre-hire Experience as a Predictor of Job Performance
Analysis
k N
̅
SDr
95% CI
SDρ
% VE
80% CV
Overall
44
11,785
.05
.12
.06
.01, .10
.13
24.92
-.11, .22
Relevance of pre-hire experience
Relevant
32
8,463
.06
.11
.07
.03, .12
.11
30.30
-.07, .21
General
18
4,165
-.02
.14
-.02
-.10, .06
.15
23.49
-.21, .17
Without outlier
17
3,576
.02
.11
.03
-.03, .09
.10
43.18
-.10, .16
Experience measure
Measurement mode
Amount
18
5,842
.02
.09
.03
-.02, .08
.09
36.39
-.08, .14
Time
31
7,381
.04
.14
.05
-.01, .11
.15
20.95
-.15, .24
Type
3
322
.01
.09
.01
-.11, .13
.00
100.0
.01, .01
Measurement level
Task
3
249
-.02
.17
-.01a
-.24, .22
.15
42.95
-.21, .19
Job
20
4,769
.08
.11
.09
.03, .15
.11
33.24
-.05, .22
Organization
5
1,739
.04
.03
.04
.02, .07
.00
100.0
.04, .04
Occupation
16
4,999
.04
.10
.05
-.01, .11
.10
34.60
-.07, .18
Industry
6
1,779
.05
.12
.06
-.06, .18
.13
22.36
-.11, .23
Without outlier
5
1,091
-.04
.08
-.04
-.13, .05
.06
67.69
-.11, .04
General
6
1,237
-.14
.13
-.17
-.29, -.05
.13
28.15
-.34, -.00
Without outlier
5
648
-.04
.11
-.04
-.16, .08
.08
64.23
-.15, .06
Job complexityb
Low
8
1,007
.16
.16
.18
.05, .31
.16
28.91
-.02, .38
Without outlier
7
763
.22
.15
.25
.12, .37
.13
38.82
.08, .42
Moderate
30
8,795
.05
.09
.07
.02, .11
.09
38.76
-.05, .18
High
13
3,491
.12
.19
.16
.04,.27
.20
11.36
-.10, .42
Without outliers
11
2,088
.25
.14
.29
.19, .38
.14
24.61
.11, .46
Performance dimension
Task performance
11
3,115
.10
.07
.11
.05, .16
.06
53.28
.03, .19
Overall performance
36
8,459
.05
.11
.06
.02, .10
.10
36.96
-.07, .20
OCB
2
231
.11
.10
.15
-.03, .32
.04
90.25
.10, .20
CWB
4
1,879
-.09
.11
-.11
-.24, .02
.12
16.55
-.26, .04
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 55
Analysis
k
N
̅
SD
r
95% CI
SD
ρ
% VE
80% CV
Without outlier
3
1,290
-.02
.05
-.02
-.09, .04
.00
99.83
-.03, -.02
Performance measure
Ratings
32
8,295
.06
.10
.08
.03, .12
.09
40.12
-.04, .20
Productivity records
12
4,305
.04
.11
.05
-.02, .12
.11
23.47
-.09, .19
Work samples
3
237
.07
.15
.08
-.11, .26
.11
58.69
-.06, .21
Publication status
Published
35
9,680
.06
.13
.07
.02, .12
.14
21.75
-.11, .24
Unpublished
9
2,105
-.01
.06
-.01
-.07, .03
.00
100.0
-.01, -.01
Note. k = number of validity coefficients from independent samples; ̅ = sample-size weighted mean observed validity; SDr =
standard deviation of rs; = mean validity estimate corrected for measurement error in job performance; 95% CI = lower and
upper bounds of the 95% confidence interval for ; SDρ = standard deviation of
ρ
� values; % VE = percentage of variance in
accounted for by sampling error and measurement error in the criterion; 80% CV = lower and upper bounds of the 80%
credibility value for . OCB = organizational citizenship behavior. CWB = counterproductive work behavior.
a In a few instances, is slightly smaller than ̅ because the individuals rs in the meta-analysis are positive and negative. In this
particular case, a positive r for one primary study increased more than the negative rs from the other primary studies, which
resulted in a slightly less negative .
b This analysis also includes some studies that measured training performance rather than job performance.
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 56
Table 2
Meta-analytic Results for Pre-hire Experience as a Predictor of Training Performance
Analysis
k N
̅
SDr
95% CI
SDρ
% VE
80% CV
Overall
21
8,176
.09
.14
.11
.04, .18
.15
13.95
-.08, .31
Experience measurea
Measurement mode
Amount
16
6,542
.07
.11
.09
.04, .15
.10
26.05
-.03, .22
Time
6
2,051
.15
.17
.16
-.01, .32
.20
8.70
-.09, .41
Without outlier
5
1,591
.20
.16
.22
.05, .39
.19
9.89
-.02, .46
Measurement levelb
Task
9
2,159
.27
.12
.32
.23, .40
.11
27.07
.17, .46
Job
3
720
.00
.08
.00
-.10, .11
.06
60.76
-.08, .08
Occupation
7
4,173
.05
.04
.06
.03, .10
.00
100.0
.06, .06
Performance measure
Ratings
9
4,776
.05
.11
.06
-.04, .16
.14
14.50
-.12, .24
Training exams
9
2,552
.13
.16
.14
.03, .26
.17
14.07
-.07, .35
Publication status
Published
13
4,273
.09
.18
.11
.00, .23
.20
10.04
-.14, .36
Unpublished
8
3,903
.09
.08
.11
.05, .18
.07
37.91
.02, .20
Note. k = number of validity coefficients from independent samples; ̅ = sample-size weighted mean observed validity;
SDr = standard deviation of rs; = mean validity estimate corrected for measurement error in training performance; 95%
CI = lower and upper bounds of the 95% confidence interval for ; SDρ = standard deviation of
ρ
� values; % VE =
percentage of variance in accounted for by sampling error and measurement error in the criterion; 80% CV = lower
and upper bounds of the 80% credibility value for .
a All training studies measured relevant pre-hire experience; no studies measured prior general experience.
b Only one study measured industry-level experience, and no studies measured organization- or general-level experience.
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 57
Table 3
Meta-analytic Results for Pre-hire Experience as a Predictor of Turnover
Analysis
k N
̅
SDr
95% CI
SDρ
% VE
80% CV
Overall
32
11,676
.00
.10
.00
-.04, .04
.10
22.30
-.13, .13
Relevance of pre-hire experience
Job-relevant
19
5,006
.02
.11
.03
-.02, .08
.09
29.94
-.09, .15
General
17
7,573
.00
.11
.00
-.06, .06
.11
15.08
-.14, .14
Experience measure
Measurement mode
Amount
17
5,544
-.01
.13
-.02
-.08, .05
.13
15.66
-.18, .15
Time
14
3,081
.04
.09
.05
-.00, .10
.07
50.43
-.04, .13
Tenure in prior jobs
8
4,506
.04
.07
.05
-.01, .11
.07
26.54
-.04, .14
Type
2
283
.01
.01
.01
-.01, .03
.00
100.0
.01, .01
Measurement level
Job
19
7,909
-.01
.09
-.01
-.05, .04
.09
23.67
-.12, .10
Organization
3
1,393
.01
.08
.01
-.10, .11
.08
25.49
-.10, .11
Occupation
10
2,301
.03
.13
.03
-.05, .12
.12
22.71
-.12, .19
Industry
4
1,097
-.06
.06
-.07
-.13, -.01
.00
100.0
-.07, -.07
General
6
1,298
.07
.10
.07
-.01, .16
.08
40.54
-.03, .18
No outlier
5
501
.16
.11
.17
.07, .27
.06
71.19
.09, .25
Outcome measure
Turnover intentions
12
3,796
-.01
.11
-.00
-.07, .07
.11
24.27
-.14, .14
No outliers
10
2,179
.08
.08
.08
.03, .14
.05
67.60
.02, .15
Actual turnover
24
9,058
.00
.10
.00
-.04, .04
.09
26.47
-.11, .11
Voluntary
10
2,037
.04
.17
.03
-.08, .14
.17
15.14
-.18, .25
Involuntary
5
1,404
-.01
.09
.01
-.09, .10
.09
29.59
-.11, .13
Publication status
Published
22
9,191
.00
.10
-.00
-.05, .04
.10
19.43
-.13, .13
Unpublished
10
2,485
.00
.10
.01
-.06, .08
.09
33.19
-.11, .12
Note. k = number of validity coefficients from independent samples; ̅ = sample-size weighted mean observed validity; SDr =
standard deviation of rs; = mean validity estimate corrected for deviation from a 50% base rate in turnover and from measurement
error in turnover intentions; 95% CI = lower and upper bounds of the 95% confidence interval for ; SDρ = standard deviation of
ρ
�
ρ
ˆ
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 58
values; % VE = percentage of variance in accounted for by sampling error and measurement error in the criterion; 80% CV =
lower and upper bounds of the 80% credibility value for . Positive correlations indicate that higher levels of pre-hire experience
are related to lower turnover intentions and actual turnover.
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 59
Table 4
Illustrative Range Restriction-Corrected Correlations between Pre-hire Experience and
Outcomes
u = .75
u = .85
u = .95
Outcome
k
̅
rr rr rr
Job performance overall
44
.05
.06
.09
.08
.07
1-3 months on job
4
.19
.22
.32
.28
.25
6-12 months on job
6
.07
.08
.11
.10
.09
12 months or longer on job
4
.04
.05
.06
.05
.05
Training performance
21
.09
.11
.14
.12
.11
Turnover
32
.00
.00
.00
.00
.00
Note. ̅ = sample-size weighted mean observed validity; = validity estimate corrected for
measurement error in the outcome; u = range restriction ratio; rr = validity estimate corrected
for measurement error in the outcome and indirect range restriction in the predictor.
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
META-ANALYSIS OF PRE-HIRE EXPERIENCE 60
Table 5
Meta-analytic Results for Relations between Pre-hire Experience and other Predictors
Analysis
k N
̅
SDr
95% CI
SDρ
% VE
80% CV
Age
34
9,594
.25
.25
.25
.17, .34
.25
4.96
-.06, .57
Amount of experience
21
6,725
.24
.29
.24
.11, .36
.29
3.22
-.14, .61
Time of experience
18
5,019
.35
.20
.35
.26, .44
.19
7.12
.11, .60
Type of experience
2
210
.06
.05
.06
-.01, .14
.00
100.0
.06, .06
Cognitive ability
12
3,401
.06
.12
.07
-.01, .15
.12
26.15
-.08, .22
Job knowledge
6
2,156
.15
.04
.19
.14, .23
.00
100.0
.19, .19
Personality
Agreeableness
5
1,873
-.05
.12
-.07
-.18, .05
.12
20.37
-.21, .08
Emotional stability
7
2,106
.02
.07
.02
-.04, .08
.04
68.21
-.03, .08
Extraversion
6
1,987
.03
.11
.03
-.07, .12
.11
25.46
-.11, .16
Conscientiousness
9
2,453
.05
.06
.05
.00, .10
.00
100.0
.05, .05
Openness to experience
6
2,544
.01
.07
.01
-.05, .07
.05
55.95
-.05, .08
Self-efficacy
3
1,351
.07
.16
.08
-.11, .28
.16
9.04
-.13, .29
Person-environment fit
Person-job fit
3
998
.10
.10
.10
-.03, .23
.10
25.45
-.02, .23
Person-organization fit
3
998
.12
.05
.13
.06, .19
.01
94.44
.11, .15
Note. k = number of validity coefficients from independent samples; ̅ = sample-size weighted mean observed validity;
SDr = standard deviation of rs; = mean validity estimate corrected for measurement error in the other predictor, except
for experience-age relations, which were not corrected for measurement error in age; 95% CI = lower and upper bounds
of the 95% confidence interval for ; SDρ = standard deviation of
ρ
� values; % VE = percentage of variance in
accounted for by sampling error and measurement error in the criterion; 80% CV = lower and upper bounds of the 80%
credibility value for .
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
ρ
ˆ
