Performance-Approach Goal Effects Depend on How They Are Defined: Meta-Analytic Evidence From Multiple Educational Outcomes

Abstract

Achievement goal theory originally defined performance-approach goals as striving to demonstrate competence to outsiders by outperforming peers. The research, however, has operationalized the goals inconsistently, emphasizing the competence demonstration element in some cases and the peer comparison element in others. A meta-analysis by Hulleman et al. (2010) discovered that students’ academic achievement was negatively predicted by performance-approach goals that focus on appearing talented, but positively predicted by performance-approach goals that focus on outperforming peers. The present meta-analysis extends that pattern to numerous other educational outcomes, such as competence perceptions and self-regulation. It does so while also removing a confound (i.e., the sample’s mean age) that varies systematically along with the type of performance-approach goal measure employed in studies. Discussion explores when and why the two types of performance-approach goals are most likely to diverge versus converge. It also considers two potential directions that goal theory can take to incorporate the two performance-approach goals.

Full text

Performance-Approach Goal Effects 1
Performance-Approach Goal Effects Depend on How They Are Defined:
Meta-Analytic Evidence from Multiple Educational Outcomes
Corwin Senko & Blair Dawson
State University of New York – New Paltz
In press, Journal of Educational Psychology
Address correspondence to:
Corwin Senko
Associate Professor
Psychology Department
State University of New York – New Paltz
New Paltz, NY 12561
Tel. 845-257-3602
Email: senkoc@newpaltz.edu

Performance-Approach Goal Effects 2
Abstract
Achievement goal theory originally defined performance-approach goals as striving to
demonstrate competence to outsiders by outperforming peers. The research, however, has
operationalized the goals inconsistently, emphasizing the competence demonstration element in
some cases and the peer comparison element in others. A meta-analysis by Hulleman et al.
(2010) discovered that students’ academic achievement was negatively predicted by
performance-approach goals that focus on appearing talented, but positively predicted by
performance-approach goals that focus on outperforming peers. The present meta-analysis
extends that pattern to numerous other educational outcomes, such as competence perceptions
and self-regulation. It does so while also removing a confound (i.e., the sample’s mean age) that
varies systematically along with the type of performance-approach goal measure employed in
studies. Discussion explores when and why the two types of performance-approach goals are
most likely to diverge versus converge. It also considers two potential directions that goal theory
can take to incorporate the two performance-approach goals.
Key Words: Achievement Goals, Performance-Approach Goals, Meta-Analysis
Educational Impact And Implications Statement
Many students strive to demonstrate competence (to teachers, peers, or family) by outperforming
their peers, but is this goal good or bad for their learning experience? This meta-analysis
provides an answer: it depends on whether the goal emphasizes the ‘demonstrating competence’
element or the ‘outperforming peers’ element. The former element interferes with learning by
encouraging help-avoidance or excuse-making, but the latter element facilitates learning by
supporting effective strategies and bolstering confidence. These findings advance our
understanding of students’ achievement goals. Teachers may also find them useful if curious
about whether and how to motivate students through competitions.

Performance-Approach Goal Effects 3
Performance-Approach Goal Effects Depend on How They Are Defined:
Meta-Analytic Evidence from Multiple Educational Outcomes
What motivates students? Are some motivations better than others? If so, what can be
done to promote them? Achievement goal theory traces the answers to students’ goals (Dweck,
1986; Nicholls, 1984). It contrasts mastery goals and performance goals, each reflecting a unique
reason for engaging in a task. Students pursuing mastery goals strive to develop competence by
maximizing their potential, improving on prior success, or simply learning to heart’s content.
Those pursuing performance goals, by contrast, strive to demonstrate existing competence,
typically by outperforming peers or by matching their success with less effort. Each goal is
approach-based, meaning they strive toward acquiring success. Theorists later added to each goal
an avoidance counterpart that strives to avoid failure: mastery-avoidance goals that aim to
prevent a decline in skill or a failure to learn, and performance-avoidance goals that aim to
prevent a display of low competence relative to others (Elliot & Harackiewicz, 1996; Elliot &
McGregor, 2001; Middleton & Midgley, 1997; Skaalvik, 1997; Vandewalle, 1997).
In testing achievement goal theory, the vast majority of studies have correlated students’
self-reported goals with their achievement or other learning-related outcomes. Each goal’s
effects are well-documented in several reviews (e.g., Moller & Elliot, 2006; Senko, Hulleman, &
Harackiewicz, 2011) and meta-analyses (Baranik, Stanley, Bynum, & Lance, 2010; Cellar et al.,
2011; Huang, 2011a, b; Hulleman, Schrager, Bodmann, & Harackiewicz, 2010; Lochbaum &
Gottardy, 2015; Payne, Youngcourt, & Beaubein, 2007; Van Yperen, Blaga, & Postmes, 2014,
2015; Wirthwein, Sparfeldt, Pinquart, Wegerer, & Steinmayr, 2013). Mastery-approach (MAp)
goals provide a wide array of benefits – for example, positive emotions (e.g., positive affect and
hope) and interest, elaborative learning strategies and effective self-regulation, and help-seeking
and cooperativeness, to list just a few. The two avoidance goals – mastery-avoidance (MAv) and
performance-avoidance (PAv) – have shown sporadic benefits too. For example, some studies
have found that MAv boost interest (see Baranik et al., 2010), and other recent studies suggest
that avoidance goals may prove beneficial in late adulthood (Senko & Freund, 2015) or for East
Asian students (King, 2016). Notwithstanding those limited benefits, however, both of the
avoidance goals have proven maladaptive overall, each consistently producing a wide array of
detrimental outcomes, such as negative emotions (e.g., anxiety and hopelessness), poor learning
strategies, unwillingness to seek help, poor health, openness to cheating, and so forth.
Performance-approach (PAp) goal effects, by contrast, produce highly inconsistent and
even contradictory effects: for example, anxiety and negative affect, but also pride and positive
affect (see Huang, 2011a); self-consciousness (e.g., Heintz & Steele-Johnson, 2004), but also
task focus (e.g., Lee, Sheldon, & Turban, 2003); effort-withdrawal and self-handicapping (e.g.,
Midgley, Arunkumar, & Urdan, 1996), but also high effort intensity and challenge-seeking (see
Senko, Durik, Patel, Lovejoy, Valentiner, & Stang, 2013); “shallow” learning strategies (i.e.,
rehearsal), but also “deep” strategies (i.e., elaboration) and self-regulation (see Payne et al.,
2007). Perhaps the PAp goal’s most consistent effect – and certainly its most controversial
(Brophy, 2005; Harackiewicz, Barron, & Elliot, 1998; Midgley, Kaplan, & Middleton, 2001;
Senko et al., 2011) – has been on academic achievement. PAp goals positively predict
achievement (Hulleman et al., 2010). What explains this bewildering pattern for PAp goals?
Recent research suggests that it may trace to inconsistencies in how researchers define
PAp goals (Hulleman et al., 2010; Senko & Tropiano, 2016). The remainder of this paper further
probes this possibility. We present a meta-analysis that tests if PAp goal effects on various

Performance-Approach Goal Effects 4
educational outcomes depend on how the goal is operationally defined. Note that we shall
narrow our focus solely onto PAp goals, for they are the ones that have produced mixed effects
and spurred controversy. MAp, MAv, and PAv goals, by contrast, all provide largely consistent
results, their only noteworthy controversy being about the prevalence of MAv goals (Hulleman
& Senko, 2010).
The Performance Goal’s Essence: Appearing Talented or Outperforming Others?
Achievement goal theory is now over 30 years old, yet theorists disagree over what
exactly is a PAp goal (Grant & Dweck, 2003; Kaplan & Maehr, 2007; Urdan, 1997; see Elliot,
2005, for a historical review). Is its essence to demonstrate competence? Or to outperform peers?
Goal theory’s founders emphasized competence demonstration (Dweck, 1986; Maehr,
1984; Nicholls, 1984). From their view, an achievement goal represents the broad reason for
engaging in an achievement task: either to develop competence (MAp goals) or to demonstrate
competence (PAp goals). But they also featured peer comparison in their definitions to varying
degrees. This practice reflects an assumption that the twin desires to appear competent and to
outperform peers naturally cohere; arousing one will prime the other. For example, contexts
emphasizing competition may prompt students to strive to demonstrate high ability compared to
others (Ames, 1992), and, conversely, students eager to showcase high ability may try to do so
by outperforming others (Nicholls, 1984). The early research therefore defined PAp goals with
either or both of these two elements, tacitly assuming they should produce the same effects. Still,
according to the goal theory’s initial framework, called the “goal orientation model”, competence
demonstration takes the lead role in this partnership, and it should create a maladaptive
orientation to the task – one marked by self-consciousness, anxiety, and challenge-avoidance.
Elliot (Elliot & Thrash, 2001) later offered a rival framework called the “goal standard
model.” In line with classic theories of goals, he proposed that an achievement goal must
emphasize competence attainment, defined either with personal standards such as improving on
prior success (MAp goals) or with interpersonal standards (PAp goals). From that view,
outperforming peers is the PAp goal’s true essence. Equally important, students may have
various motives for trying to outperform peers. One motive might be to showcase their talent,
much like the goal orientation model assumes. But students might also strive to outperform peers
for other reasons unrelated to impression management, such as the enjoyment of the challenge
(Urdan & Mestas, 2006; Vansteenkiste, Lens, Elliot, Soenens, & Mouratidis, 2014). So,
according to the goal standard model, we cannot assume that outperforming others and appearing
talented are interchangeable desires. They may overlap but still are conceptually distinct.
It appears they may be empirically distinct too (Edwards, 2014; Hackel, Jones,
Carnonneau, & Mueller, 2016; Senko & Tropiano, 2016; Warburton & Spray, 2014). Hulleman
et al.’s (2010) demonstrated this best in a meta-analysis of achievement goal correlations with
academic achievement and interest. Their sweep of nearly 100 studies identified numerous PAp
goal measures, some widely-used (e.g., Button, Mathieu, & Zajac, 1996; Elliot & McGregor,
2001; Midgley et al., 2001; Vandewalle, 1997) and others custom-made for specific studies.
Hulleman et al. coded the thematic content of every item in each measure, and then classified
each goal measure according to its dominant theme. In some PAp goal measures, the
predominant theme is to appear talented (e.g., Vandewalle, 1997). But in others, it is to
outperform peers (e.g., Elliot & Church, 1997). This distinction matters. In Hulleman et al.’s
meta-analysis, PAp goals predicted low academic achievement (r = -.14) when the dominant
theme was to appear talented, but they predicted achievement gains (r = .14) when the dominant

Performance-Approach Goal Effects 5
theme was to outperform others. Given their potential differences, we will refer to these types of
PAp goals as “appearance goals” and “normative goals” for the remainder of the paper.
Though revealing, Hulleman et al.’s (2010) meta-analysis is limited in two ways that
necessitate follow-up: (a) their effect was restricted to only one outcome (academic
achievement), and (b) it might be caused by an underlying confound. We elaborate each issue
below and then test them in a new meta-analysis.
Extension #1: Do Appearance and Normative Goals Differ on Other Outcomes?
Hulleman et al. (2010) restricted their meta-analysis to only two educational outcomes,
academic achievement and interest. Of the two, only achievement showed a difference between
the appearance and normative types of PAp goals. Does that finding generalize to other
important outcomes, or is it restricted to academic achievement? It is a critical question. If the
finding generalizes to numerous outcomes, then the field must revisit how best to conceptualize
PAp goals – a thorny but necessary task that may entail revising achievement goal theory. If,
however, the finding is confined to academic achievement, such efforts may be unnecessary.
Academic achievement clearly ranks high in importance for its power to catalyze student
motivation and its role as a gateway to new opportunities. Yet it may also be a flawed indicator
of learning – or at least long-term quality of learning – because course assignments sometimes
demand only superficial topic knowledge (Kohn, 2000). High performance can therefore occur
without clear learning (i.e., relatively permanent change in behavior or knowledge), and, worse,
genuine gains in learning can go undetected by performance measures (Soderstrom & Bjork,
2015). Accordingly, one might argue that, although normative goals facilitate achievement, they
fail to aid learning beyond transient and superficial levels (e.g., Midgley et al., 2001). Perhaps,
then, any benefit of normative goals over appearance goals is unique to achievement. The two
PAp goals might produce equally weak (or negative) effects on other outcomes intimately linked
to learning, such as self-regulation or “deep” study strategies that entail elaborating or evaluating
course concepts. Of course, the other possibility is that normative goals also aid learning. After
all, academic achievement, despite its flaws, is facilitated by many learning-related outcomes
widely hailed in the field, such as self-efficacy and self-regulation (Credé & Kuncel, 2008;
Robbins, Lauver, Le, Daniels, Langley, & Carlstrom, 2004), and hindered by others widely
denounced, such as self-handicapping (Schwinger, Wirthwein, Lemmer, & Steinmayr, 2014).
Perhaps, then, normative goals, more so than appearance goals, facilitate a relatively adaptive
nomological network of thoughts, emotions, and behaviors that support learning as well as
achievement. There is clear need to compare the two PAp goals’ effects on other educational
outcomes besides achievement.
The present meta-analysis does this. We relied mostly on prior meta-analyses (Baranik et
al., 2010, Cellar et al., 2011; Huang, 2011a; Payne et al., 2007) to identify the outcome variables
for inclusion: competence perceptions, various studying-related strategies, and several positive
and negative emotions, all of which are detailed below. This list necessarily excludes several
other important outcomes that goal researchers have begun to test in recent years, most notably
social ones (e.g., peer relationship quality, feelings of belongingness), moral ones (e.g., tolerance
of cheating), or health ones (e.g., burnout). For each, we found too few studies to allow a viable
test of whether they are affected differently by appearance and normative PAp goals.
Competence Perceptions. Competence perceptions, which have always played a pivotal
role in achievement goal theory (Dweck, 1986; Elliot, 1999; Nicholls, 1984), are well-known to
facilitate task performance and related processes (Valentine, DuBois, & Cooper, 2010). Past

Performance-Approach Goal Effects 6
research shows they typically have moderately sized, positive links with PAp goals (see Baranik
et al., 2010; Cellar et al., 2010).
Yet the competence perception construct takes many forms. One well-known distinction
is between relatively stable judgments of one’s ability (e.g., academic self-concept; Marsh, 1990)
versus task-specific expectations (e.g., self-efficacy; e.g., Pajares, 1996). Those two perceptions
often correlate strongly (e.g., Bong & Skaalvik, 2003), making their distinction unlikely to
matter here. One other difference in competence perceptions may prove important for PAp goals,
however. For general ability judgments or task-specific expectancies alike, some measures
encourage respondents to compare themselves to peers (e.g., Marsh, 1992; Pintrich & De Groot,
1990), whereas other measures refer solely to the task (e.g., Elliot & Church, 1997; Midgley et
al., 2001). Normative goals might correlate more strongly with the former due to their shared
emphasis on social comparison. The more pressing issue, though, is whether normative goals
also have different effects than appearance goals on either measure of competence perceptions.
Study Strategies. Achievement goals have long been assumed to trigger different
strategies for learning and studying (e.g., Pintrich, 1999; Wolters, Yu, & Pintrich, 1996). These
include deep and surface strategies, among others. Deep strategies entail summarizing and
elaborating concepts, generating personal examples, creating analogies, asking questions,
evaluating theories, and so forth. Surface strategies entail passive note-taking and rote
memorization. PAp goals predict both strategies positively, yet more strongly so for the surface
ones (see Payne et al., 2007). As with achievement goals, however, theorists disagree about how
best to conceptualize those surface strategies (see Pintrich, 2004). Some frame them as
maladaptive strategies done out of work-avoidance or confusion or extrinsic motivation, thus
being incompatible with deep strategies and quality learning (e.g., Biggs, 1993; Entwistle &
McCune, 2004). Their inventories reflect this assumption (e.g., “I learn some things by rote,
going over and over them until I know them by heart even if I do not understand them”; Biggs,
Kember, & Leung, 2001). Other theorists instead consider the surface and deep strategies to be
independent or even overlapping, each potentially supportive of learning (e.g., Pintrich & De
Groot, 1990). Their surface strategy measures therefore emphasize only rehearsal, stripped of
any underlying motives (e.g., “I memorize key words to remind me of important concepts in this
class”; Pintrich, Smith, Garcia, & McKeachie, 1993). Given these opposing viewpoints, our
meta-analysis will distinguish the maladaptive surface strategies favored by the first perspective
from the adaptive surface strategies favored by the second perspective.
Student learning and achievement are also aided by metacognition and self-regulation,
the process of monitoring one’s own goal progress, identifying knowledge gaps, and changing
strategies if necessary (Winne, 2005). Whereas MAp goals are often touted for facilitating self-
regulation, PAp goals are not (e.g., Middleton & Midgley, 1997; Wolters, 2004). The one prior
meta-analysis of PAp goals and self-regulation found no link between them (Cellar et al., 2011).
Students may also engage in more socially-oriented study strategies that harm or aid their
learning and performance. One adaptive strategy is help-seeking, which is typically unrelated to
PAp goals (e.g., Ryan & Pintrich, 1997). Two others are maladaptive strategies done largely out
of fear of being judged incompetent. The first is help-avoidance. The other is self-handicapping:
to claim or actually produce handicaps to success, thereby allowing an external attribution in
case one performs poorly (Rhodewalt, 1990). Several studies link PAp goals to both help-
avoidance (e.g., Ryan & Pintrich, 1997) and self-handicapping (see Urdan & Midgley, 2001).
Emotions. Achievement goals should affect learning in part through the emotions they
evoke during task engagement (Linnenbrink & Pintrich, 2002; Pekrun, Elliot, & Maier, 2006).

Performance-Approach Goal Effects 7
Many studies indicate positive yet mild links between PAp goals and positive emotions, most
notably task enjoyment and generalized positive affect. However, PAp goals also have positive
and mild links with negative emotions, most notably anxiety and negative affect (for meta-
analyses, see Baranik et al., 2010, and Huang, 2011a). The present meta-analysis tests if these
seemingly contradictory patterns are partly explained by the two types of PAp goals having
different relationships with positive and negative emotions. Other emotions (e.g., pride, shame,
and boredom) were excluded because only a few eligible studies tested them, nearly all using
normative PAp goals (see Huang, 2011a, for a summary).
Extension #2: Is Sample Age Confounded with PAp Type?
The present meta-analysis extends Hulleman et al.’s (2010) in another way, too. One
limitation of any meta-analysis is that, because it cannot standardize every feature of the studies
being compared, the predictor variable (e.g., PAp goals) can become confounded with other
methodological features that also vary systematically between those studies. In acknowledging
this, Hulleman et al. noted that the goal measures used by researchers are slightly confounded
with the age of students tested. Research with young samples (i.e., elementary and middle-
school) typically have assessed appearance PAp goals, whereas research with older samples have
used an even mix of the two types of PAp goals. Might this confound explain the two goals’
different effects? Theorists have long posited that younger students, owning a more fragile sense
of self and being less accustomed to ability evaluation or social comparison, are more vulnerable
to the mild anxiety and other potential costs of PAp goals (e.g., Dweck, 1986; Midgley et al.,
2001). If true, then either type of PAp goal would produce more harmful effects with younger
samples, in which case the meta-analytic difference between the two goals could be a mirage.
We are unaware of any direct tests of this possibility. But four prior meta-analyses have tested if
the PAp goal’s link with achievement depends on students’ age or other demographics; three
show the link prevails unabated across different age groups, ethnicities and nationalities (Huang,
2011b; Hulleman et al., 2010; Van Yperen et al., 2014), while another found the effect to be
positive with college student samples but null with elementary and middle-school aged children
(Wirtheim, Sparfeldt, Pinquart, Wegerer, & Steinmayr, 2013). None disentangled age from the
different types of PAp goal measures, however. The present meta-analysis will do so.
Overview of Current Meta-Analysis
In sum, the current meta-analysis extends Hulleman et al.’s (2010) in two ways. One is to
test how well their findings generalize to numerous learning-relevant outcomes. No prior meta-
analysis has done so. The other is to test if any differences between PAp goal subtypes are due to
an underlying age confound. These two improvements allow clearer insight into how much
normative and appearance PAp goals truly differ in predictive validity – an essential foundation
for any eventual discussion within the field about whether to reconsider the PAp goal construct.
This meta-analysis is largely exploratory, but there are sensible grounds for expecting the
two PAp goals to impact some outcomes differently. If the existing link between normative goals
and academic achievement reflects quality learning, those goals may also have stronger links
than appearance goals with outcomes that most directly facilitate achievement – in particular,
competence perceptions (see Credé & Kuncel, 2008) and adaptive learning strategies, especially
self-regulation and deep learning strategies (see Robbins et al., 2004). Conversely, appearance
goals may have stronger links than normative goals with self-handicapping and help-avoidance,
both of which are driven by self-presentation concerns (Rhodewalt, 1990; Ryan & Pintrich,

Performance-Approach Goal Effects 8
1997). For the other outcomes – adaptive or maladaptive surface learning strategies, help-
seeking, and positive or negative emotions – there is no clear-cut theoretical reason for
hypothesizing stronger effects of one PAp goal over the other.
Method
Sample of Studies
We used the PsycINFO, ERIC, and Academic Search Complete databases and restricted
our search to articles published prior to August 1, 2014 in English-speaking, peer-reviewed
journals. Excluding unpublished studies may of course bias results toward stronger effects,
which have greater odds of being published (i.e., the “file drawer problem”; Rosenthal, 1979).
We will explore this issue later in the paper.
This meta-analysis includes a wide range of educational outcomes, all measured by
student self-report. One is competence perceptions, which has strong facilitative links to
academic achievement and interest (Robbins et al., 2004). Seven outcomes are specific strategies
that may either aid learning (i.e., self-regulation, deep learning strategies, adaptive surface
strategies, and help-seeking) or hinder learning (i.e., maladaptive surface strategies, self-
handicapping, and help-avoidance). The remaining four outcomes are affective, including two
that are broad in scope (i.e., positive affect and negative affect) and two that are specific
emotions (i.e., enjoyment and anxiety).
Every search necessitated achievement goal* or goal orientation* in the text, and then
was narrowed to include keywords pertaining to the relevant outcome (see Appendix 1 for the
full list of keywords). For example, the competence perception keywords included "perceived
competenc*", "competence expectanc*", "self-efficacy", and other variants). For the emotion
outcomes, we supplemented this search strategy with published articles listed in Huang’s (2011a)
meta-analysis of goals and emotions, because our initial search provided a low harvest due to
emotions seldom being of primary importance in achievement goal studies.
Eligibility Criteria
Studies were further screened for three requirements for inclusion. First, papers must
include zero-order correlations between PAp goals and at least one of the outcomes. Second,
papers must include sample sizes to appropriately weight their effect sizes (Lipsey & Wilson,
2001). Third, we must have access to the complete PAp goal measure. Most studies used
established goal measures, in which case they were included so long as the version of the
measure was clear. When studies used customized measures and provided only sample items, we
contacted the authors to request the full measure.
Final Sample of Studies
This meta-analysis included 296 studies, cumulating in 314 independent samples totaling
115,250 participants (56% female). Most samples were from North America (k = 169), followed
by Europe (k = 89), Asia (k = 45), the Middle East (k = 9), and South America (k =2). Most also
were tested in educational settings (k = 253), with the remaining in sport (k = 45) or occupational
(k = 16) settings.
Sample age is an important control variable for the moderator analyses described later.
Most papers provided it. Others instead provided the grade level, which we used to estimate
sample age based on established grade-age norms. In cases where the sample comprised a range

Performance-Approach Goal Effects 9
of grades but the reported effects were aggregated across the sample (e.g., 7th – 12th grades;
Murayama & Elliot, 2009), we used the median grade level as a conservative estimate of the
sample’s age (across all studies, M = 18.5 years, SD = 6.3). Most studies requiring that strategy
were of college or adult samples; very few studies of younger students needed it.
Goal Coding
Classifying Individual Items. The set of studies in this meta-analysis included 48 unique
PAp goal measures. Using Hulleman et al.’s (2010) typology, we coded the thematic content of
each item in every PAp goal measure. Appearance items emphasize appearing intelligent to
others (e.g., “One of my goals is to show others that I’m good at my class work”; Midgley et al.,
2000). Normative items emphasize only interpersonal success (e.g., “It is important for me to do
better than other students”; Elliot & McGregor, 2001)). Some items combined the appearance
and normative elements (e.g., “It’s important to me that I look smart compared to others in my
class”; Midgley et al., 2000). In those cases, we followed Hulleman et al.’s (2010) protocol of
assigning them to the PAp-Appearance category because, based on the logic of the goal
orientation model, the normative standard is pursued in service of the overarching goal to appear
talented.1 Finally, General items capture themes other than outperforming others or appearing
talented. In most cases, the item focused on obtaining a desirable grade or outcome (e.g., “The
main reason I do my work in science is because we get grades”; Anderman, Griesinger, &
Westerfield, 1998). In other cases, it focused on avoiding challenge or uncertainty (e.g., “I like to
be fairly confident that I can successfully perform a task before I attempt it”; Button et al., 1996).
All coding of items was done by the two authors, who remained blind to specific findings while
coding. Agreement rates were high across the 223 PAp goal items (91%; Cohen’s Kappa =
87%). Discrepancies were resolved through discussion.
Our coding scheme does depart from Hulleman et al.’s (2010) in one way. They
differentiated between “goal” items and “no goal” items. Goals represent a future-based and
competence-relevant endpoint that one either strives toward or away from (Elliot, 2005). Many
PAp goal measures include goal language by beginning statements, “My goal is to…”, or “One
of my aims is to…”, or “It is important to me to…” or “I am trying…” Other measures lack goal
language and instead emphasize affective engagement (e.g., “I feel successful at school when I
do the work better than other students”, italics added for emphasis; Skaalvik, 1997) or a
preference between two contrasting options (e.g., “I prefer to do things that I can do well rather
than things that I do poorly”, italics added for emphasis; Button et al., 1996). Such items were
coded as No Goal by Hulleman et al. (2010). When adopting their approach in our preliminary
analyses, the No Goal category accounted for 28% of all PAp goal items. We considered this
high rate unsatisfying because those items, despite lacking goal language, often still include clear
themes relevant to the three PAp goal subtypes. For example, the item above by Skaalvik (1997)
emphasizes normative success, whereas the one by Button et al. (1996) emphasizes challenge-
avoidance (i.e., a general goal) rather than competence demonstration or normative success. To
mix such items into a separate No Goal category ignores those thematic distinctions, making this
category too broad for meaningful comparison in analyses. Therefore, we coded each item for its
thematic content, regardless of whether it fit the strict goal definition. Approximately 60% of the
items that would have been classified as No Goal were instead classified as normative (e.g., “I
am happy only when I am one of the best in class”; McInerney, Yeung, & McInerney, 2001), and
the rest were reclassified nearly equally as either appearance (e.g., “To be honest, I really like to

Performance-Approach Goal Effects 10
prove my ability to others”; Vandewalle,1997) or general (e.g., “Getting a good grade in this
class is the most satisfying thing for me right now”; Pintrich et al., 1993).2
Classifying Full Measures. After coding all individual items, we computed how much
(proportionally) each PAp goal measure captures appearance, normative, or general themes. For
example, Skaalvik’s (1997) 5-item PAp goal measure was scored 20% Appearance, 80%
Normative, and 0% General. Each measure therefore had multiple separate Percentile Variable
scores – one apiece for each PAp goal subtype, all summing to 100%.
Then we also classified each goal measure according to its predominant theme.
Skaalvik’s (1997) measure, for example, was classified as predominantly normative. If multiple
themes of PAp goals were equally represented in a measure (e.g., Roeser, Midgley, & Urdan,
1996), or if no particular subgroup accounted for more than 50% of the measure (e.g., Greene,
Miller, Crowson, Duke, & Akey, 2004), the measure was labeled as NoMajority. Table 1
provides frequencies and representative measures for each Majority Scale.
Analytic Strategy
All effect sizes were correlations coefficients. Following established procedures (Lipsey
& Wilson, 2001), those coefficients were Fischer-z transformed to weight them by sample size,
with inverse variance weights used during data analysis. Correlation effect sizes reported herein
were reconverted with the inverse of the Fisher transformation. All analyses applied a random
effects model and were done with Lipsey and Wilson’s (2001) macros for SPSS.
Some studies provided multiple correlations for the PAp goal’s link with an outcome
variable. This was sometimes due to the same participants completing either (a) the same goal
and outcome measures multiple times in a longitudinal study (e.g., Gutman, 2006), (b) the same
goal and/or outcome measure separately for different classes (e.g., Vogler & Bakken, 2007), or
(c) multiple measures tapping the same broad outcome construct, such as state and trait measures
of anxiety (e.g., Elliot & McGregor, 1999). In those cases, the effect sizes for each goal were
aggregated into a single effect to preserve data independence (Lipsey & Wilson, 2001). The lone
exception to this rule was for the few studies that directly compared the effects of different types
of PAp goal measures (Day, Radosevich, & Chasteen, 2003; Grant & Dweck, 2003; Heidemeier
& Bittner, 2012; Potosky, 2010; Smith, Duda, Allen, & Hall, 2002), in which case the separate
effect sizes were retained for analyses because we wished to compare the goal measures too.
Results
Overall Effects
Analyses proceeded in two phases. The first examined overall effect sizes for the
relationships (i.e., zero-order correlations) between PAp goals and each outcome variable. Table
2 provides the PAp goal’s overall effect sizes (weighted r) and corresponding z-scores and
significance levels, and 95% confidence intervals (CI). Following Cohen’s (1992) guidelines,
these effect sizes can be considered small at r = .10, medium at r = .30, and large at r > .50.
PAp goals predicted all outcomes except help-seeking. Some of their effects are
undesirable: PAp goals significantly predicted maladaptive surface strategies (r = .14), self-
handicapping (r = .07), and help-avoidance (r = .11), plus negative affect (r = .11) and anxiety (r
= .13). Other effects, in contrast, are desirable: PAp goals significantly predicted general
competence perceptions (r = .19), self-regulation (r = .10), and deep learning (r = .15) and
adaptive surface strategies (r = .21) strategies, as well as positive affect (r = .11) and task

Performance-Approach Goal Effects 11
enjoyment (r = .15). Note as well that PAp goals predicted both types of competence measures,
whether in reference to the task (r = .15) or in comparison to peers (r = .28), though the latter
effect is stronger. Putting these assorted effects side-by-side shows a highly mixed and even
contradictory pattern. For example, PAp goals predicted higher negative affect and anxiety, but
those effects are offset by equally modest effects on positive affect and enjoyment. Likewise,
they predicted greater help-avoidance and self-handicapping, but also greater use of deep
strategies and self-regulation.
Moderator Analyses
The second phase of analyses tested the main research question: Do the PAp goal’s
effects sizes depend on how the goal is operationally defined? Indeed, as shown in Table 2, all
PAp goal effects showed sizable and significant heterogeneity (Qw), indicating variability due to
factors that differ systematically between studies. Moderator analyses tested if the varied
thematic content of the PAp goal measure helps account for this heterogeneity.
This was done by partitioning the sample of studies based on the majority scale code, and
then comparing the effects of each PAp goal subtype. We used a weighted least squares meta-
regression procedure so that we could include sample age as a covariate (Lipsey & Wilson,
2001). The regression model included age plus three dummy codes that compared the normative
goal against the other subtypes (i.e., appearance, general, and NoMajority). The normative goal
was chosen as the key comparison group because it aligns with the goal standards model, while
the other PAp goal measures typically were from studies anchored to the traditional goal
orientation model. Significant positive effects for those dummy codes indicate that the
relationship between the PAp goal and outcome was weaker for the normative subtype than the
comparison PAp subtype. As shown in Table 1, 26 (54%) of the PAp goal measures were
normative and 13 (27%) were appearance. The general and NoMajority categories were far less
common; for some outcomes, in fact, they included zero or only one study (see Table 3). In those
cases, the regression model excluded the corresponding dummy code(s).
Table 3 provides mean effect sizes (r) and number of studies (k) for each type of PAp
goal. Table 4 provides data for the PAp goal type comparisons, including beta coefficients (β), z-
scores and significance levels, plus unstandardized regression coefficients (B) and their
corresponding 95% confidence intervals (CI). Analyses are summarized below separately for
each PAp subtype comparison.
Normative vs. Appearance PAp Goals. Normative and appearance PAp goals produced
different patterns for several outcomes, all in ways that indicate a more adaptive profile for
normative goals. Specifically, normative goals provided stronger benefits than appearance goals
to self-regulation (rs = .16 vs. -.03; β = -.43), deep learning strategies (rs = .17 vs. .05; β = -.30),
and adaptive surface learning strategies (rs = .23 vs. .07; β = -.40). For each, the normative
goal’s effect was significant and positive, while the appearance goal’s effect was null. Both goals
positively predicted overall competence perceptions, but the normative goal did so more strongly
(rs .25 vs. .13; β = -.25). Importantly, this was true no matter if the competence perception
measure emphasized social comparison (rs .31 vs. .15; β = -.33) or the task only (rs .20 vs. .13; β
= -.18). Likewise, the appearance goals had stronger effects than normative goals on two
undesirable outcomes, self-handicapping (rs =.16 vs. .03; β = .48) and help-avoidance (rs =.16
vs. .05; β =.47. For both, the appearance goal’s effect was significant and positive, while the
normative goal’s effect was null.

Performance-Approach Goal Effects 12
Neither of these two PAp goals predicted help-seeking. Also, although maladaptive
surface strategies seem more tied to appearance goals than normative goals (rs = .42 vs. .05), that
comparison is non-significant and unreliable because only two studies used the appearance goal.
Finally, the two PAp goals also did not differ in their effects on any of the positive or negative
emotion outcomes, all of which were positively predicted by both goals.
Normative vs. General PAp Goals. Few studies used general goals (Table 3). So for
most outcomes, normative goals and general goals did not produce different patterns. The lone
exceptions were that normative goals had a positive and stronger link than general goals with
overall competence perceptions (rs = .25 vs. .01; β = -.33) – including those ‘purer’ ones
measured solely in reference to the task (rs = .20 vs. -.02; β = -.41) – yet also a null and weaker
link with help-seeking (rs = -.01 vs. .21; β = .43), though the latter should be treated with
skepticism because only two help-seeking studies used general goals.
Normative vs. NoMajority PAp Goals. The No Majority category captures a
combination of normative, appearance, and general goals, with none the most salient. The
broadness of this category renders it imprecise. Fortunately, only four measures fit this category
(Table 1), making this category poorly suited for moderator tests. Indeed this type of PAp goal
failed to produce different effects from normative goals on any outcome.
Age Effects. The analyses above show that normative and appearance goals yield many
different effects. We tested if any of these differences are explained by an underlying age
confound. Across the full sample of studies, sample age did not correlate with the %PAp-
Normative goal measure (r = -.04), but it did correlate with the %PAp-Appearance goal measure
(r = -.14, p < .05). Thus, as in Hulleman et al.’s (2010) meta-analysis, the more that the PAp goal
measure emphasized competence demonstration, the younger the study’s sample was overall.
We therefore included sample age as a covariate in the moderator analyses described
above. Sample age did moderate some overall PAp goal effects: the PAp goal’s links with
competence perceptions, deep strategies, help-seeking, and positive affect were stronger for
younger samples (see Table 4). Nevertheless, those age effects are separate from the normative
versus appearance PAp goal effects, which remain even when controlling for sample age.
Publication Bias Analysis
This meta-analysis included only published articles. In general, that approach risks
inflating effect size estimates due to journals favoring statistically significant findings
(Rosenthal, 1979). We considered the risk on conceptual, statistical, and comparative grounds.
Conceptually, the risk of publication bias seems unlikely here because, of the many
reasons for rejecting a manuscript, a null PAp goal effect is unlikely to be one. After all, PAp
goals should produce mostly null or undesired effects, according to goal theory. Regardless, even
if excluding unpublished studies does inflate these effect sizes, we consider this only a minor
limitation because, unlike in the typical meta-analysis, estimating an accurate overall effect size
is not our aim. Rather, our aim is to discern if the inconsistent PAp goal effects in published
research are due to differences in how researchers have measured PAp goals.
Nevertheless, we statistically examined if the overall PAp goal effect sizes could be due
to publication bias.3 Because those effect sizes hover in the ‘small’ range (r = .10, or d = .20;
Cohen, 1992), if they were substantially inflated by publication bias, then the true population
effect sizes must verge on zero. Orwin’s (1983) fail-safe formula allows a calculation of the
number of additional unpublished studies needed to reduce the significant overall PAp goal
effect sizes to null levels (i.e., dcriterion = .01). As shown in Table 2, this value is so large –

Performance-Approach Goal Effects 13
ranging from 274 studies for self-handicapping to 7,692 studies for competence perceptions –
that it is implausible that the current effect sizes were heavily inflated by publication bias.
Finally, it is useful to compare the overall effect sizes with those from previous meta-
analyses, each of which included unpublished studies and thus should be relatively free of the
bias (Baranik et al., 2011; Cellar et al., 2011; Huang, 2011a; Payne et al., 2007). Table 5
provides this comparison for any of our outcomes that were tested in at least one other meta-
analysis; the list includes all but surface learning strategies, self-handicapping, and help-
avoidance. Three things stand out. First, the prior meta-analyses are generally consistent with
one another, except that Baranik et al. (2011) found stronger overall effects of PAp goals on
competence perceptions and positive affect. Second, the present findings are consistent with the
prior meta-analyses, too, in terms of both the direction and the overall size of the effects. The
only notable exceptions were that our effects for self-regulation (r = .10) and enjoyment (r = .15)
were stronger than Cellar et al.’s (2011) and Huang’s (2011a), respectively. Third, for every
outcome in this comparison, our sample size of studies (k) was two to six times larger than those
used in prior meta-analyses, as should be expected given the timeline of publication dates. The
last two points, together, impart some confidence in the reliability of the present findings – and
also in those findings from the prior meta-analyses that relied on a small number of studies.
Discussion
Achievement goal theory has amassed copious research in over 30 years. In that time,
PAp goals have spawned an ongoing debate about their positive potential (Brophy, 2005;
Harackiewicz et al., 1998; Midgley et al., 2001), a debate anchored to their surprising and unique
mix of harmful and beneficial effects (Senko et al., 2011).
Our meta-analysis further confirms those mixed effects. PAp goals predicted many
educational outcomes – in fact, all those within our catalog except help-seeking. Curiously, as in
several prior meta-analyses (Baranik et al., 2010; Cellar et al., 2011; Huang, 2011a; Payne et al.,
2007), these effects appear contradictory. PAp goals show an adaptive profile in some respects:
students pursuing them report higher competence perceptions, greater use of self-regulation and
deep and adaptive surface learning strategies, and also more positive affect and enjoyment, all
with small effect sizes. Yet these same goals also show a maladaptive profile: students pursuing
them report more self-handicapping, help avoidance, and maladaptive surface strategies, as well
as more negative affect and anxiety, again all with small effect sizes.
What explains this mixed pattern for PAp goal effects? Several possible moderators have
been proposed, usually in the form of a “matching effect.” For example, theorists posit that PAp
goals can be beneficial for adult students or on simpler tasks, but become harmful for young
students or on challenging tasks (e.g., Grant & Dweck, 2003; Midgley et al., 2001). Neither idea
has strong evidence, however. Consider the PAp goal’s storied link with achievement. Three
meta-analyses show that effect holds true across different ages, ethnicities, and continents
(Huang, 2001b; Hulleman et al., 2010; Van Yperen, Blaga, & Postmes, 2014); another shows
that the effect becomes null, rather than harmful, with young students (Wirtheim et al., 2013).
The effect on achievement has also been found on challenging tasks (see Senko et al., 2013).
The present study examined another possibility that has emerged in recent years –
namely, that some of those mixed findings for PAp goals trace to differences in how researchers
define these goals (Senko et al., 2011). The easiest way for a meta-analysis to test this is to
compare prominent goal measures, such as the Achievement Goal Questionnaire (AGQ; Elliot &

Performance-Approach Goal Effects 14
Church, 1997) and the Patterns of Adaptive Learning Survey (PALS; Midgley et al., 1997), as
has been done in prior meta-analyses of academic achievement (Huang, 2011b; Hulleman et al.,
2010; Van Yperen et al., 2014; Wirtheim et al., 2013) or emotions (Huang, 2011a). But that
approach has two shortcomings. One is that it is exclusionary. It disallows tests of the many
other goal measures custom made by researchers; that approach would exclude 25% of the
studies in our meta-analysis, for example. The other is that it is imprecise. Both the AGQ and the
PALS, by far the most used goal measures, have been revised multiple times, each iteration
further narrowing the PAp goal’s definition in order to hew closer to the researchers’ guiding
framework. The AGQ originally included items that tap both normative and appearance goals
(Elliot & Church, 1997), but now, in line with the goal standard model, it taps only normative
goals (Elliot & Murayama, 2008; Elliot et al., 2011). The PALS originally emphasized normative
and non-goal themes (Midgley et al., 1997), but now, in line with the goal orientation model, all
of its items emphasize appearance themes while some also tap normative themes (Midgley et al.,
2000). Simply comparing these measures, without taking into account their many revisions,
cannot afford the precision needed to test if normative and appearance goals truly differ.
Recognizing this, Hulleman et al. (2010) painstakingly coded every measure’s individual
items, and then classified each measure based on the predominant theme running through those
items. Their results are provocative: Measures emphasizing normative strivings positively
predicted high academic achievement, whereas those emphasizing competence demonstration
negatively predict achievement. But the two types of PAp goals had identical null effects on
interest, the other outcome tested by Hulleman et al. Given that academic achievement may
sometime be a poor proxy for learning (Soderstrom & Bjork, 2015), some goal theorists (e.g.,
Brophy, 2005) speculate that normative goals facilitate only achievement and provide various
other costs that mitigate against the gains in achievement. Is the benefit of normative goals – and
any contrasting effects of normative versus appearance goals – confined to achievement?
No, it is not. Using Hulleman et al.’s (2010) coding scheme on 48 different PAp goal
measures, we found that their finding generalizes to many other educational outcomes.
Normative goals predicted high competence perceptions and only adaptive strategies (self-
regulation, plus deep and adaptive surface strategies), whereas appearance goals predicted only
maladaptive strategies (self-handicapping and help-avoidance). The differential patterns were not
as robust as Hulleman et al.’s (2010); normative and appearance goals had opposing positive
versus negative links with achievement in their study, but the two PAp goals generally had null
versus significant effects on the outcomes studied here (see Table 3). Still, a clear pattern
emerges across both meta-analyses: quite simply, normative goals seem more adaptive than
appearance goals.
There were two clear exceptions to this pattern, however. One is that neither goal
predicted help-seeking, though nor was either expected to. The other is that normative and
appearance goals share similar relationships with emotions; that, too, is unsurprising, because
there is little theoretical rationale to expect these goals to promote different emotions. What does
surprise, however, is that both PAp goals positively predicted positive emotions (positive affect
and enjoyment) and negative emotions (negative affect and anxiety) alike, and to the same small
degree. One possibility is that PAp goals produce positive and negative emotions simultaneously.
This “mixed feelings” explanation, however, assumes that positive and negative emotions
correlate positively with one another, but in fact they usually correlate negatively or not at all
(e.g., Pekrun et al., 2006, 2009; Watson et al., 1988). Furthermore, when the two do co-occur, it
is in atypical situations (e.g., transitions, or succeeding while witnessing a friend fail) and more

Performance-Approach Goal Effects 15
likely in East Asian cultures due to their more dialectic patterns of thinking (Miyamoto, Uchida,
& Ellsworth, 2010). The mixed feelings measured in such studies are general positive and
negative affect or happiness-dejection emotions. Anxiety (a prospective outcome-based emotion)
and enjoyment (a current task-based emotion), two specific emotions predicted by PAp goals,
diverge even more and seem to us less compatible. An alternate explanation is that, in any study,
some students pursuing PAp goals experience positive emotions, and others negative emotions.
If true, one wonders what predicts the direction their emotions take. We can only speculate. One
possibility is that it depends on timing and confidence. In the initial learning experience (e.g.,
early semester), activity-based emotions like enjoyment (or boredom) predominate, and it may
be that PAp goals (like MAp goals) promote mostly positive emotions then. But as assignments
come into focus, emotions become highly contingent on the outcome, especially for students
pursuing PAp goals (Pekrun et al., 2006). Those anticipating success will likely experience
positive outcome-based emotions like hope and pride; those less confident in success will
experience negative outcome-based emotions like anxiety. This remains for future research to
test directly.
The competence perception and surface learning outcomes each merit special attention
because of how they are measured. Some competence perception measures refer solely to
confidence in doing a task well (e.g., Elliot & Church, 1997; Midgley et al., 2001), whereas
others also encourage respondents to compare ability levels with peers (e.g., Marsh, 1992;
Pintrich & De Groot, 1990). The two do overlap a great deal (Bong & Skaalvik, 2003), and our
findings show that both types correlate more strongly with normative goals than appearance
goals. Still, the normative goal’s effect size is stronger when competence perception measures
emphasize social comparison. Likewise, mastery goals appear to have stronger effects on self-
efficacy measures that emphasize learning or improvement rather than social comparisons
(Senko & Hulleman, 2013). Such findings spotlight the potential for either goal’s effect to be
inflated due to overlap in content between the goal and outcome measures.
Measures of the surface learning strategy are inconsistent as well. Some researchers (e.g.,
Pintrich et al., 2003) define this strategy solely in terms of rehearsal efforts, and they characterize
it as adaptive and compatible with deep learning and self-regulation strategies. Other researchers
(e.g., Biggs et al., 2001) define it as rehearsal done out of confusion or work-avoidance, and they
characterize it as maladaptive and incompatible with the other strategies. Goal researchers have
historically ignored this distinction, however, leading many to interpret the PAp goal’s oft-cited
link to surface learning as a maladaptive effect. Separating the two measures in our analyses
showed that normative goals predicted only the adaptive type of surface strategy (as well as the
deep learning and self-regulation strategies), and that it does so much more strongly than
appearance goals.4
A second purpose of this meta-analysis was to unconfound goal measures and sample
age. In ours and Hulleman et al.’s (2010) meta-analyses, appearance goals, but not normative
goals, were more likely to be used in research on younger students. It is plausible, therefore, that
any maladaptive effects found for appearance goals could really be due to younger students
being less comfortable with any type of PAp goal. The present study used a meta-regression
technique that tested the independent effects of PAp goal subtypes versus sample age. Age did
moderate some of the overall PAp goal effects; the goal’s links with competence perceptions,
deep learning strategies help-seeking, and positive affect were all more positive for younger
samples. Yet age did not diminish or explain any of the significantly different effects of
normative versus appearance goals.

Performance-Approach Goal Effects 16
This meta-analysis included two other PAp goal categories as well: General goals and
NoMajority goals. General goals were a catch-all category for items not emphasizing normative
comparisons or competence demonstration. They instead emphasized either avoiding challenge
or, in most cases, attaining desired outcomes (e.g., wanting to do well or, in academic contexts,
to get a good grade). Those measures (e.g., Pintrich et al., 1993), created early in goal theory’s
development, were often referred to as outcome (or extrinsic) goals, a label that reflects the
initially blurred lines between achievement goal theory and other theories (e.g., Deci & Ryan,
1985; Zimmerman & Kitsantas, 1997) that contrast adaptive (mastery goals, intrinsic motivation,
process focus) versus maladaptive (performance goals, extrinsic motivation, outcome focus)
motivations. Outcome goals are certainly common, perhaps much more so than MAp or PAp
goals. But their conceptual value is less clear. They focus on the rewards of achievement more
than attaining competence itself. In principle, these broader outcome goals could be achieved
through task mastery or by outperforming others, and indeed some studies show that outcome
goals offer little unique predictive value beyond those two traditional achievement goals (e.g.,
Grant & Dweck, 2003).5 Most achievement goal theorists (e.g., Elliot & Thrash, 2001; Midgley
et al., 2000) therefore abandoned outcome goals and defined PAp goals solely with appearance
or normative themes. Consequently, there were too few studies using general PAp goal measures
to include in most of our moderator analyses. In fact, only two educational outcomes included
more than five studies with general goals, and the one reliable effect is that the general goals
were significantly less likely than normative goals to predict competence perceptions. The final
PAp goal subtype, the NoMajority goal category, applied to measures in which the items
sampled too broad a range of themes for any one of them (e.g., normative comparison) to
predominate. There were only a few such measures (e.g., Greene et al., 2004) and too few studies
using them to allow meaningful tests of its unique impact on any outcomes.
Limitations
This meta-analysis is limited in several respects. Two concern statistical power. First, due
to so few studies using appearance goals in tests of maladaptive surface strategies or enjoyment,
we recommend caution in interpreting their effect sizes for that goal. Fortunately, those two tests
seem to be the only underpowered ones among the main analyses; the number of studies in each
of our overall analyses far surpass prior meta-analyses (see Table 5), and the remaining
outcomes had sufficient number of studies to permit reliable comparisons of appearance and
normative goals (see Table 3). Second, this meta-analysis excluded some important outcomes
because there were too few studies overall, or else because virtually all studies used only the
normative PAp goal. So we cannot know if the patterns shown here extend to, for example,
interpersonal competencies (e.g., collaboration, belongingness), moral judgments (e.g.,
cheating), or student well-being (aside from emotions). For the same reason, we also excluded all
established goal antecedents – whether features of the student (e.g., beliefs about ability being
fixed, trait-level anxiety, perfectionism), the task (e.g., novelty, difficulty), the learning context
(e.g., student-teacher relationships, or ‘goal structures’ cultivated through instructional and
evaluation methods), or the broader culture (see Senko, 2016, for a review of antecedents). Such
work will be a fruitful direction for future studies.
Finally, as with the research it compiled, this meta-analysis cannot allow causal
conclusions about the relationships between goals and correlates. Are the correlates goal
outcomes? Or are they goal antecedents? They all certainly fit well as outcomes conceptually;
virtually every included study treated them that way, in fact, with the exception of anxiety

Performance-Approach Goal Effects 17
sometimes being a dispositional antecedent (see footnote 4). Moreover, we know from many
laboratory studies that experimentally-induced goals can directly impact a variety of student
outcomes, such as interest and task performance (for meta-analyses, see Rawsthorne & Elliot,
1999, and Van Yperen et al., 2015). Clearly, goals can provide causal effects. But those goals are
also dynamic and responsive to ongoing experience. It is probably best, therefore, to view many
goal-outcome links as reciprocal over time (Harackiewicz et al., 2008; King & McInerney, 2016;
Linnenbrink & Pintrich, 2002; Putwain, Larkin, & Sander, 2013; Senko & Harackiewicz, 2005;
Van Yperen & Renkema, 2008). For example, the link between normative goals and academic
achievement is bidirectional: pursuing the goal facilitates achievement due to various processes
aroused by the goal, and achievement reinforces continual pursuit of the goal (Van Yperen &
Renkema, 2008). Similar patterns may be true of the normative goal’s links with competence
perceptions, self-regulation, and deep and surface learning strategies. Likewise, appearance goals
may lead students to avoid help or to self-handicap, and the success of these strategies at
minimizing negative evaluations from teachers or peers may reinforce the continued pursuit of
that goal. Reciprocal patterns should also apply to PAv, MAp, and MAv goals, of course.
Theoretical Implications
Now that it is clear the two PAp goals differ not just conceptually but also empirically,
the field must decide how best to proceed. One option is to choose one goal (and its
corresponding achievement goal model) and abandon the other. But this is divisive and
implausible; after decades of research, both goals are too entrenched to abandon. Nor is it clear
that either the goal orientation model or the goal standard model is superior to the other, whether
on theoretical grounds or empirical grounds (Senko, 2016). The field instead needs a progressive
approach, one that incorporates appearance and normative goals. There are two ways to do this.
Comparing both PAp Goals. One way is to compare the two types of PAp goals side-
by-side. Recent studies have begun to do this (Grant & Dweck, 2003; Edwards, 2014; Hackel,
Jones, Carnonneau, & Mueller, 2016; Senko & Tropiano, 2016; Warburton & Spray, 2014).
Each confirms that normative and appearance goals separate in factor analyses and, more
importantly, that they produce different effects that match the meta-analytic findings. In samples
of high school or college students, normative goals promoted self-efficacy (Edwards, 2014;
Senko & Tropiano, 2016) and achievement (Warburton & Spray, 2014), whereas appearance
goals promoted ability attributions for failure (Grant & Dweck, 2003), effort withdrawal and
self-handicapping (Grant & Dweck, 2003; Senko & Tropiano, 2016), help-avoidance (Senko &
Tropiano, 2016), and disinterest (Edwards, 2014). Additional studies are needed to generalize
this pattern to other cultures and learning contexts.
Moving forward, we also need to learn when the two PAp goals diverge or overlap, and
why. We suspect that normative and appearance goals arouse somewhat different processes, and
that they therefore will diverge most for outcomes that are sensitive to those processes. Consider
first appearance goals. Striving to appear talented should, according to goal orientation theorists
(Maehr, 1984; Nicolls, 1984), arouse public self-consciousness and worrisome thoughts that
disrupt task focus and impair learning and achievement, especially when doing challenging tasks.
Appearance goals should predict outcomes rooted to these processes – for example, self-
handicapping and help-avoidance, as well as socially influenced emotions such as anxiety during
task engagement, shame following failure, and relief following success. Normative goals do not
share this fate. They need not arouse self-consciousness or impair task focus. Instead, owing to
their normative standard for goal attainment, they have two unique qualities. First, they are

Performance-Approach Goal Effects 18
usually challenging – more so than mastery goals, in fact (Senko & Hulleman, 2013) – because
attaining them requires outperforming most or all peers. Second, because these standards are
typically set by others (e.g., teachers), they are also rigid and outside the student’s control. These
two qualities may deter unconfident students (Van Yperen & Renkema, 2008) and arouse
moderate anxiety in students chasing these goals, but they also can promote engagement (Lee et
al., 2003) and effort (Elliot et al., 1999), attune students to task demands (Senko, Hama, &
Belmonte, 2013), and proffer positive outcome-based emotions such as hope or pride (Pekrun et
al., 2006). Studies are needed to explore these possibilities.
This basic principle should also apply to the antecedents of each PAp goal. Perhaps
appearance and normative goals correlate more strongly, and produce similarly undesirable
effects, when sharing a potent antecedent. For example, the two might converge more in heavily
evaluative contexts. Many students are likely to pursue appearance goals in such conditions, and
those also pursuing normative goals probably do so because they believe that outperforming
peers will impress evaluators. The two goals work in concert in those cases – exactly as the
original goal orientation model proposed (Ames, 1992; Dweck, 1986; Nicholls, 1984). The
added challenge of the normative standard (i.e., having to outperform most others) might even
amplify the inimical processes aroused by appearance goals. Recent experiments by Sideridis et
al. (2014) offer tentative evidence. Participants completed a novel task in a highly evaluative
context, and those given a very difficult normative goal (i.e., to outperform everyone else who
has done the study) suffered more anxiety than those not given the normative goal. Perhaps the
same normative goal, however, would produce more beneficial effects if pursued in less
evaluatively threatening contexts – and therefore without the accompaniment of an appearance
goal. Senko and Harackiewicz (2002) found preliminary evidence for this. Their participants
pursuing normative goals were more engaged and interested when doing the task within a
relatively neutral context instead of an evaluatively threatening one. Those two papers suggest
that some contexts summon appearance and normative goals together, while others allow
students more freedom to pursue normative goals without appearance goals. Similar patterns
might emerge for other goal antecedents, even dispositional ones. For example, perhaps the two
goals overlap more among test-anxious students than other students.
Comparing the two goals has obvious appeal. First, it is simple methodologically.
Second, it sharpens the field’s focus on processes elicited by PAp goals. And third, it is
conciliatory, adopting a ‘big tent’ approach rather than insisting the field continue with only one
of these two PAp goals. Notwithstanding these positives, however, we believe this approach’s
long-term potential is limited in one crucial way: it fails to truly integrate the goal orientation and
goal standard frameworks that created the two different PAp goals.
Integrating both PAp Goals. The better long-term option, in our opinion, would
integrate rather than compare the two PAp goals. There may be several ways to do so (e.g., Korn
& Elliot, 2016). The leading contender at this juncture is the goal complex model (Elliot &
Thrash, 2001; Urdan, 2000; Vansteenkiste et al., 2014). Essentially a compromise between the
goal orientation and goal standard models, this model assumes that goal standards (interpersonal
for PAp goals, intrapersonal for MAp goals) are the ‘true’ achievement goal, but it also assumes
that these goals can be pursued for a variety of reasons and that those reasons help shape the
achievement goal’s effects. Put another way, a goal complex is a hierarchy of two goals: a
higher-order one focused on the broad purpose or reason for engaging in a task, in line with the
goal orientation model, plus a slightly lower-order goal focused on the standard for defining
success when engaging the task, in line with the goal standard model. According to this model, a

Performance-Approach Goal Effects 19
PAp goal standard (outperforming others) serves the higher-order reason, the two together
creating a ‘goal complex’ that guides student experience. Thus, a normative goal can be pursued
in order to appear talented, and it is likely to in such cases to have the maladaptive effects long
theorized for PAp goals. But the same normative goal can also be pursued for others reasons,
such as pride or enjoyment of competing, and its effects in those cases may be more adaptive.
Several recent studies of goal complexes support this premise (e.g., Dompnier, Darnon, &
Butera, 2013; Gillet, Lafrenière, Vallerand, Huart, & Fouquereau, 2014; Michou, Vansteenkiste,
Mouratidis, & Lens, 2014; Senko & Tropiano, 2016; Vansteenkiste, Mouratidids, & Lens, 2010).
Indeed, it seems typical for students who pursue normative goals to do so for healthier reasons
than a desire to appear talented (Michou et al., 2014; Senko & Tropiano, 2016; Urdan & Mestas,
2006). This might explain why normative goals generally are more beneficial than appearance
goals on the whole. New research is needed to further probe this possibility, to chart the full
range of PAp goal complexes, and to identify the triggers of each. In fact, all of the research
directions suggested above for comparing normative and appearance goals can also be
accomplished with the goal complex model.
Conclusion
It is now clear that normative goals and appearance PAp goals often differ, the former
behaving more adaptively overall than the latter. Looking forward, the field must determine
whether to study the two goals together or to integrate them somehow, as with a goal complex
model. In the meantime, we encourage researchers to use meaningful goal labels that specify the
type of PAp goal used (e.g., “normative” or “appearance”) and also their overarching
achievement goal model (i.e., goal standards or goal orientations). This practice will ensure
greater alignment between a study’s methods and guiding theoretical model. It will also allow
readers to more easily distinguish the two PAp goals, compartmentalize their respective findings,
and generate clearer new directions.

Performance-Approach Goal Effects 20
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Performance-Approach Goal Effects 38
Footnotes
1. Hulleman et al. (2010) labelled these “evaluative” goals but then aggregated them with the
appearance goals. We eschewed the “evaluative” goal label for simplicity. They were rare in
this meta-analysis (24 of 223 items) and, with one exception (Grant & Dweck, 2003), never
comprised the majority of a PAp goal measure’s items.
2. Although there are many NoGoal measures, those measures have been used far less
frequently than Goal-based measures. Consequently, for nearly all outcomes, there were only
between 0-3 studies for NoGoal versions of the different PAp types (e.g., NoGoal-
Normative). This prevents reliable comparisons among those NoGoal versions. The one
exception was competence perceptions, for which there were over a dozen studies of the
NoGoal versions of each PAp type; the basic finding described later for the full set of studies
was duplicated even when restricting the analysis to those NoGoal measures.
3. These analyses focus on the overall effect size of PAp goals because, if there is a
publication bias, it should apply equally to all subtypes of PAp goals.
4. A few other outcomes also varied in how they were measured, but those differences
proved to be irrelevant in this meta-analysis. First, competence perception measures vary in
another respect separate from the task versus social comparison distinction. Some
competence perception measures emphasize judgments of current ability, while others
emphasize prospective expectancies (e.g., self-efficacy). Second, some measures of self-
handicapping capture task-specific handicapping (e.g., Urdan & Midgley, 2001), while others
capture more general tendencies to handicap (e.g., Rhodewalt, 1990), though both types were
treated as goal outcomes in virtually all studies in the meta-analysis. Third, some measures of
anxiety emphasize state-like feelings that are modeled as goal outcomes, while others
emphasize trait-like feelings that are modeled it as a goal antecedent (Spielberger, 1989). The
same is also possible for negative and positive affect (Watson, Clark, & Tellegen, 1988), but
nearly all studies in the meta-analyses treated those measures as goal outcomes.
Supplemental analyses showed that the key findings (i.e., normative vs. appearance goal
comparisons) reported in this paper held true for either version of each of these outcome
measures.
5. In fact, on the rare occasion that outcome goals are tested nowadays, they are classified by
some goal orientation theorists as a type of performance goal (e.g., Bong, Woo, & Shin,
2013; Sideridis, Kaplan, Papadopoulos, & Anastasiadis, 2014) and by some goal standards
theorists as a type of mastery goal (Tuominen-Soini, Salmelo-Aro, & Niemivirta, 2012). In
both cases, the outcome goal is defined by what it is not – as a performance goal because it
lacks the mastery goal’s focus on learning within the goal orientation model, or as a mastery
goal because it lacks the performance goal’s focus on competition within the goal standard
model.

Performance-Approach Goal Effects 39
Table 1
Frequencies of Each Performance-Approach (PAp) Goal’s Majority Code Scales in the Meta-Analysis
PAp Goal Subtype
Normative
Appearance
General
No Majority
# Measures
26
13
5
4
# Samples (K)
210
65
29
10
Sample Items
“My goal in this class is to get
a better grade than most of the
students” (Elliot & Church,
1997)
“Doing better than other
students in class is important to
me” (Midgley et al., 1997)
“One of my goals is to show
others that I’m good at my
class work” (Midgley et al.,
2000).
“I try to figure out what it takes
to prove my ability to others at
work” (Vandewalle, 1997)
“It is important for me to
establish a good overall grade-
point” (Harackiewicz et al.,
2000)
“Getting a good grade in this
class is the most satisfying
thing for me right now”
(Pintrich et al., 1993)
Representative
Measures
Elliot & Church (1997)
Midgley et al. (1997)
Skaalvik (1997)
Midgley et al. (2000)
Spinath & Steinmayr (2012)
Vandewalle (1997)
Anderman et al. (1998)
Button et al. (1996)
Pintrich et al. (1993)
Greene et al. (2004)
Roeser et al. (1996)
Notes. Samples (K) refers to number of independent samples in the entire meta-analysis, and it varies between each outcome
tested. Measures categorized as No Majority include a combination of the three themes – normative, appearance, or general –
without any theme the most dominant.

Performance-Approach Goal Effects 40
Table 2
Results of Analyses of Overall Effects of Performance-Approach Goal on All Outcomes
Outcome
K
Total N
Weighted r
z-score
95% CI -
LB
95% CI -
UB
QW
Fail Safe N
Competence Perceptions
204
83,256
.19***
15.13
.17
.22
2528.11***
7,692
Relative to Task
132
42,114
.15***
10.56
.12
.18
1146.80***
3,873
Relative to Peers
72
41,142
.28***
14.16
.24
.32
956.12***
4,128
Learning Strategies
Self-Regulation
39
14,408
.10***
3.33
.04
.16
461.38***
745
Deep Strategy
48
17,605
.15***
6.71
.11
.19
367.64***
1,408
Adaptive Surface Strategy
27
8,090
.21***
6.14
.14
.27
215.41***
1,133
Maladaptive Surface Strategy
11
4,493
.14*
2.10
.01
.264
166.17***
300
Self-Handicapping
21
7,319
.07*
2.44
.01
.13
109.15***
274
Help-Avoidance
18
5,694
.11***
4.26
.06
.16
55.55***
380
Help-Seeking
20
4,385
.04
1.27
-.02
.10
75.722***
140
Emotions
Negative Affect
26
8,317
.11***
6.01
.08
.15
57.96**
549
Anxiety
73
22,825
.13***
9.25
.11
.16
304.00***
1,816
Positive Affect
22
7,252
.11****
4.40
.06
.16
84.92***
465
Enjoyment
35
12,063
.15***
5.94
.10
.20
230.70***
1,027
Notes. K = number of independent effect sizes in meta-analysis. N = number of participants. All effect sizes (weighted r) and corresponding z-scores and
confidence intervals assume a random effects model. Z-scores above 1.96 and confidence intervals excluding zero are statistical significant (p < .05). LB and
UP are lower and upper bounds of the 95% confidence interval (CI). QW indexes the heterogeneity of the effect size. Fail-Safe N indicates number of
unpublished studies needed to reduce overall effect size to zero.
* p < .05, ** p < .01, *** p < .001.

Performance-Approach Goal Effects 41
Table 3
Mean Effect Sizes for All PAp Goal Subtypes
PAp Goal Subtype
Normative
Appearance
General
No Majority
Outcome
K
N
Weighted
r
K
N
Weighted
r
K
N
Weighted
r
K
N
Weighted
r
Competence Perceptions
125
59,877
.25***
47
15,842
.13***
24
5,585
.01
8
1,952
.20**
Relative to Task
73
24,586
.20***
34
11,662
.13***
22
5,264
-.02
3
602
.14
Relative to Peers
52
35,291
.31***
13
4,180
.15***
2
321
.35**
5
1,350
.23**
Study Strategies
Self-Regulation
23
10,351
.16***
12
3,082
-.03
3
541
.08
1
434
.18
Deep Strategy
34
13,690
.17***
11
3,153
.05
3
762
.06
0
Adaptive Surface Strategy
19
7,076
.23***
6
636
.07
2
378
.33***
0
Maladaptive Surface Strategy
8
3,482
.05
2
333
.42
1
678
.43**
0
Self-Handicapping
12
3,414
.03
7
3,557
.16**
1
285
.14
1
63
-.16
Help-Avoidance
9
2,366
.05
8
2,885
.16***
0
1
443
.17
Help-Seeking
7
1,191
-.01
11
2,821
.04
2
373
.21*
0
Emotions
Negative Affect
14
5,917
.09***
10
1,975
.12***
1
248
.15
1
177
.27**
Anxiety
57
17,568
.14***
7
1,583
.14**
6
2,957
.08
3
717
.09
Positive Affect
15
6,111
.12***
6
893
.11*
1
248
.01
0
Enjoyment
30
9,335
.14***
2
442
.15
3
2,286
.20***
0
Notes. K = number of independent effect sizes in meta-analysis. N = number of participants. All effect sizes (weighted r) use a random effects model.
* p < .05, ** p < .01, *** p < .001.

Performance-Approach Goal Effects 42
Table 4
Moderator Analyses of PAp Goal Subtypes
PAp Goal Comparisons
95% CI
Outcome
QB
β
B
z-score
LB
UB
Competence Perceptions
63.58***
Normative vs. Appearance
-.25***
-.11
3.94
-.17
-.06
Normative vs. General
-.33***
-.20
5.06
-.27
-.12
Normative vs. No Majority
-.06
-.06
1.05
-.18
.05
Sample Age
-.23***
-.01
3.57
-.02
-.01
Relative to Task
39.89***
Normative vs. Appearance
-.18*
-.07
2.20
-.14
-.01
Normative vs. General
-.41***
-.20
4.78
-.28
-.11
Normative vs. No Majority
-.07
-.07
0.78
-.26
.11
Sample Age
-.19*
-.01
2.27
-.01
.00
Relative to Peers
18.81***
Normative vs. Appearance
-.33***
-.16
3.34
-.26
-.07
Normative vs. General
.07
.09
0.72
-.15
.33
Normative vs. No Majority
-.12
-.09
1.22
-.24
.06
Sample Age
-.25**
-.01
2.59
-.02
-.01
Study Strategies
Self-Regulation
7.07
Normative vs. Appearance
-.43**
-.19
2.64
-.32
-.05
Normative vs. General
-.09
-.07
0.59
-.32
.17
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.04
-.01
0.38
-.02
.01
Deep Strategy
7.56
Normative vs. Appearance
-.30*
3
2.19
-.24
-.02
Normative vs. General
-.14
-.10
1.13
-.27
.07
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.27*
-.01
1.99
-.02
-.01
Adaptive Surface Strategy
4.89
Normative vs. Appearance
-.40*
-.19
2.07
-.36
-.01
Normative vs. General
.13
.09
0.69
-.17
.39
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.14
-.01
0.74
-.03
.02

Performance-Approach Goal Effects 43
Maladaptive Surface Strategy
9.05
Normative vs. Appearance
.35
.24
0.90
-.18
.75
Normative vs. General
.39
.28
1.19
-.18
.74
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.23
-.01
0.54
-.05
.03
Self-Handicapping
4.79
Normative vs. Appearance
.48*
.13
2.01
.01
.25
Normative vs. General
--
--
--
--
--
Normative vs. No Majority
--
--
--
--
--
Sample Age
.13
.00
0.54
-.01
.02
Help-Avoidance
5.42
Normative vs. Appearance
.47*
.10
1.98
.01
.20
Normative vs. General
--
--
--
--
--
Normative vs. No Majority
--
--
--
--
--
Sample Age
.11
.01
0.46
-.01
.01
Help-Seeking
15.65**
Normative vs. Appearance
.07
.02
0.39
-.09
.14
Normative vs. General
.43**
.23
2.39
.04
.42
Normative vs. No Majority
n/a
--
--
--
--
--
Sample Age
-.57***
-.01
3.32
-.02
-.01
Emotions
Negative Affect
0.39
Normative vs. Appearance
.06
.01
0.30
-.07
.09
Normative vs. General
--
--
--
--
--
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.11
.00
0.58
-.01
.01
Anxiety
2.44
Normative vs. Appearance
-.02
-.01
0.15
-.10
.09
Normative vs. General
-.16
-.07
1.36
-.17
.03
Normative vs. No Majority
-.07
-.04
0.56
-.20
.11
Sample Age
.09
.00
0.46
-.01
.01
Positive Affect
14.77***
Normative vs. Appearance
.09
.03
0.62
-.07
.14
Normative vs. General
--
--
--
--
--
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.63***
-.02
3.84
-.02
-.01
Enjoyment
1.25

Performance-Approach Goal Effects 44
Normative vs. Appearance
.03
.01
0.14
-.21
.25
Normative vs. General
.19
.09
0.98
-.09
.27
Normative vs. No Majority
--
--
--
--
--
Sample Age
-.17
-.01
0.86
-.02
.01
Notes. QB is variability in the overall effect size accounted for by differences among the PAp goal subtypes. β and B
are standardized (beta) and unstandardized regression coefficients, respectively. LB and UB refer to lower and upper
bounds of the 95% confidence interval (CI). Significant negative coefficients for the goal comparisons indicates that
the PAp goal’s effect size is stronger for the Normative type than the comparison PAp goal subtype. Dashes indicates
the dummy code was omitted from the regression = not available due to insufficient number of studies.
* p < .05, ** p < .01, *** p < .001.

Performance-Approach Goal Effects 45
Table 5
Comparison of Overall PAp Goal Effect Sizes in Present and Prior Meta-Analyses
Correlate
Present Findings
Payne et al. (2007)
Baranik et al. (2010)
Huang (2011a)
Cellar et al. (2011)
Competence Perceptions
r = .19 (k = 204)
r = .03 (k = 44)
r = .26 (k = 8)
--
r = .10 (k = 21)
Self-Regulation
r = .10 (k = 39)
--
--
r = .01 (k = 10)
Deep Strategy
r = .15 (k = 48)
r = .13 (k = 23)
--
--
Help-Seeking
r = .04 (k = 20)
r = -.01 (k = 10)
r = .03 (k = 9)
--
Negative Affect
r = .11 (k = 26)
--
r = -.03 (k = 11)
r = .08 (k = 8)
--
Anxiety
r = .13 (k = 73)
r = .16 (k = 15)
--
r = .12 (k = 28)
--
Positive Affect
r = .11 (k = 22)
--
r = .14 (k = 5)
r = .06 (k = 7)
--
Enjoyment
r = .15 (k = 34)
--
--
r = .04 (k = 6)
--
Notes. Effect sizes (r) are sample-weighted correlation coefficients. Correlates in present study unlisted here have not been tested in prior meta-
analyses. k = number of independent effect sizes in meta-analysis. Dashes mean the outcome was not tested in that meta-analysis.

Performance-Approach Goal Effects 46
Appendix
Search terms used for each outcome, all in conjunction with “goal orientation*” or “achievement
goal*”. To expand the search, “*” endings allow any variation of ending to the root term.
Competence Perceptions:
self-efficacy, perceived competenc*, competence expectanc*,
academic self-concept, academic expect*
Study Strategies:
learning strateg*, superficial learn*, superficial process*,
superficial approach, superficial strateg*, surface learn*, surface
process*, surface approach, surface strateg*, shallow learn*,
shallow process*, shallow approach, shallow strateg*, memoriz*,
rehears*, deep learn*, deep process*, deep approach, deep
strateg*, elaborat*, critical think*
Self-Regulation
self-regulat*, metacognit*
Self-Handicapping:
self-handicap*, impression manage*
Help-Avoidance
& Help-Seeking:
help-avoid*, help threat*, help-seek*, feedback seek*, feedback
accept*
Emotions:
emotion, negative affect*, anxiety, fear, worry, positive affect*,
enjoyment