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Unique Effects of Setting Goals on Behavior Change: Systematic Review
and Meta-Analysis
Tracy Epton
University of Manchester
Sinead Currie
University of Stirling
Christopher J. Armitage
University of Manchester and NIHR Manchester Biomedical Research Centre
Objective: Goal setting is a common feature of behavior change interventions, but it is unclear when goal
setting is optimally effective. The aims of this systematic review and meta-analysis were to evaluate: (a)
the unique effects of goal setting on behavior change, and (b) under what circumstances and for whom
goal setting works best. Method: Four databases were searched for articles that assessed the unique
effects of goal setting on behavior change using randomized controlled trials. One-hundred and 41 papers
were identified from which 384 effect sizes (N⫽16,523) were extracted and analyzed. A moderator
analysis of sample characteristics, intervention characteristics, inclusion of other behavior change
techniques, study design and delivery, quality of study, outcome measures, and behavior targeted was
conducted. Results: A random effects model indicated a small positive unique effect of goal setting
across a range of behaviors, d⫽.34 (CI [.28, .41]). Moderator analyses indicated that goal setting was
particularly effective if the goal was: (a) difficult, (b) set publicly, and (c) was a group goal. There was
weaker evidence that goal setting was more effective when paired with external monitoring of the
behavior/outcome by others without feedback and delivered face-to-face. Conclusions: Goal setting is an
effective behavior change technique that has the potential to be considered a fundamental component of
successful interventions. The present review adds novel insights into the means by which goal setting
might be augmented to maximize behavior change and sets the agenda for future programs of research.
What is the public health significance of this article?
The findings reported in the present review show that goal setting is an effective behavior change
technique that can be considered a fundamental component of successful behavior change interventions.
Findings suggest that optimally goals should be: (a) difficult but achievable, (b) set publicly, (c) set face
to face, (d) set as a group goal, and (e) set without drawing attention to goal commitment. There is also
some indication that goal setting is particular effective in certain samples (i.e., schoolchildren, general
population, male, younger people, and those of Asian ethnicity) in particular settings (i.e., schools and
workplaces).
Keywords: goal setting, behavior change, randomized controlled trials, meta-analysis, systematic review
Supplemental materials: http://dx.doi.org/10.1037/ccp0000260.supp
A goal is “the object or aim of an action” (Locke & Latham,
2002, p. 705) and goal setting is one of the fundamental techniques
that public bodies and government agencies recommend to pro-
mote behavior change (e.g., NHBLI, 2000;NICE, 2014). Goal
setting is considered to be a key element in helping individuals to
regulate their own behavior and has been used in numerous fields
including education (e.g., Bandura & Schunk, 1981), sport (e.g.,
Anshel, Weinberg, & Jackson, 1992), health (e.g., Alexy, 1985),
social behaviors (e.g., Madera, King, & Hebl, 2013), production
(e.g., Jackson & Zedeck, 1982), and the environment (e.g., Baca-
Motes, Brown, Gneezy, Keenan, & Nelson, 2013). Goal setting is
a commonly used behavior change technique: A recent review of
interventions designed to increase physical activity found that goal
setting was the third most often used technique with 34% (26 out
of 76) of the interventions including a goal setting component
(Conn, Hafdahl, Phillips, Ruppar, & Chase, 2014). However, de-
spite the popularity of goal setting as a technique to be included in
behavior change interventions, and several meta-analyses that ex-
plore the effects of goal setting on behavior change (Conn et al.,
Tracy Epton, Manchester Centre for Health Psychology, University of
Manchester; Sinead Currie, Division of Psychology, University of Stirling;
Christopher J. Armitage, Manchester Centre for Health Psychology, Uni-
versity of Manchester, and NIHR Manchester Biomedical Research Centre.
We thank Christine Rowland for her help with data coding and Johannes
Schiebener for providing additional data. This research was supported by
the NIHR Manchester Biomedical Research Centre.
Correspondence concerning this article should be addressed to Christo-
pher J. Armitage, Manchester Centre for Health Psychology, University of
Manchester, Coupland I, Coupland Street, Oxford Road, Manchester, M13
9PL. E-mail: chris.armitage@manchester.ac.uk
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Consulting and Clinical Psychology © 2017 American Psychological Association
2017, Vol. 85, No. 12, 1182–1198 0022-006X/17/$12.00 http://dx.doi.org/10.1037/ccp0000260
1182
2014;Chidester & Grigsby, 1984;Kleingeld, van Mierlo, & Ar-
ends, 2011;Mento, Steel, & Karren, 1987;Neubert, 1998;Tubbs,
1986;Wood, Mento, & Locke, 1987), it is not yet clear what are
the unique effects of goal setting across multiple behaviors, as
previous meta-analyses were largely based on the organizational
psychology literature, nor how goal setting can be optimized to
maximize behavior change. The aims of the present systematic
review and meta-analysis are to discover: (a) the unique effects of
goal setting on behavior change across a range of behaviors, and
(b) under what circumstances and for whom goal setting works
best.
Goal Setting Theory
Goal setting theory (Locke & Latham, 2002,2006) was derived
from a series of industrial/organizational psychology experiments
regarding work-related task performance (Locke & Latham, 2002).
The original theory posits that goal setting will promote behavior
change when two conditions are met: (1) the goal must be con-
scious and specific; and (2) the goal must be sufficiently difficult
(i.e., over and above what is usually achieved).
The idea that goals should be “conscious and specific” (e.g.,
“reduce hill running time by 7 seconds”) can be contrasted with
general intentions or vague goals such as “do your best” goals
(e.g., “I will do my best at hill running”). Reviews of studies that
compared specific goals with “do your best” or no goals found a
medium sized effect (the overall effect sizes found by the various
meta-analyses range from d⫽.42 to d⫽.56) for goal setting on
behavior or performance (Chidester & Grigsby, 1984;Kleingeld et
al., 2011;Mento et al., 1987;Tubbs, 1986;Wood et al., 1987). The
idea that goals should be “sufficiently difficult” is supported by
evidence showing medium to large effects on behavior/perfor-
mance (the overall effect sizes found by meta-analyses range from
d⫽.44 to d⫽.82) of setting difficult goals compared with setting
easy goals (Chidester & Grigsby, 1984;Kleingeld et al., 2011;
Mento et al., 1987;Tubbs, 1986;Wood et al., 1987).
Theoretical Moderators
Goal setting theory proposes four moderators of the goal setting-
behavior relationship (Latham & Seijts, 2016;Locke & Latham,
2002;Locke & Latham, 2006). Thus, the positive relationship
between goal setting and behavior is hypothesized to be enhanced
when: (a) people are more committed to the goal; (b) the task is
low in complexity (i.e., the number of acts and decisions required
to reach the goal is low; Wood, 1986); (c) feedback is received
regarding progress toward the goal (Locke & Latham, 2002); and
(d) there are adequate situational resources/few situational con-
straints (Latham & Seijts, 2016).
To date, the effect of goal commitment and situational con-
straints/resources on goal setting have not been subject to meta-
analytic review, but task complexity and feedback have. Task
complexity has been the focus of two reviews, one of which found
that increased task complexity decreased the effect of goals on
individual performance (Wood et al., 1987). The second review
found that task complexity was not a moderating factor for goals
that were set for groups rather than individuals (Kleingeld et al.,
2011). The implication is that the combined capabilities of people
in groups exceeds that of solo individuals, meaning that groups are
better equipped to handle complex tasks; this reflects the premise
that the moderating effect of task complexity is only likely to occur
when the person does not have sufficient ability (Locke & Latham,
1990).
Feedback has also been found to be a significant moderator of
the relationship between goal setting and behavior change when
studies compare a goal setting plus feedback condition with a goal
setting only condition (the overall effect sizes found by the two
meta-analyses are d⫽.56 and d⫽.63; Neubert, 1998;Tubbs,
1986). In contrast with the research reviewed above, in which
provision of feedback was experimentally manipulated, meta-
analyses that coded for the presence or absence of feedback
produced mixed results. For example, Mento, Steel, and Karren
(1987) found no effects of feedback whereas Chidester and
Grigsby (1984) found strong positive effects of accompanying
goal setting with feedback.
The Present Review
Reviews to date suggest that goal setting may be effective in
changing behavior, but suffer a number of limitations, including:
(a) focusing on a limited range of studies (i.e., a selected behavior,
such as work-related goals, health/therapy goals, or only group
goals; Chidester & Grigsby, 1984;Kleingeld et al., 2011;Matre,
Dahl, Jensen, & Nordahl, 2013;Mento et al., 1987;Shilts,
Townsend, & Dishman, 2013;Tubbs, 1986;Wood et al., 1987);
(b) including correlational studies so causation cannot be inferred
(Chidester & Grigsby, 1984;Kleingeld et al., 2011;Mento et al.,
1987;Tubbs, 1986); (c) including only published studies meaning
that publication bias might account for the patterns of findings; and
(d) not exploring a range of theoretical and practical moderators.
The present meta-analysis addresses these issues by assessing
studies that have uniquely tested goal setting on behavioral or
performance tasks, and excluding correlational studies from the
analyses.
For a behavior change technique to be optimal it is important to
determine under which circumstances and for whom goal setting is
most effective in changing behavior. This might include exploring
the effect of different types of goals, the correspondence between
the goal and dependent variable and the effect of adding other
behavior change techniques related to goal setting. The following
discussion considers the potential moderators of the effects of goal
setting on behavior/performance.
Proposed Moderators
Behavioral versus outcome goals. Goal setting theory
(Locke & Latham, 2002,2006) makes a distinction between goals
that focus on behavior and goals that focus on outcomes. For
example, someone who wishes to lose weight may set a behavioral
goal of not snacking between meals or an outcome goal of losing
one pound of weight per week. More recently, experts involved in
the development of the Behavior Change Technique Taxonomy
version 1 (BCTTv1; Michie et al., 2013) make a similar distinction
between these two types of goal setting. However, the taxonomy is
limited in so far as the reasons for the proposed distinction be-
tween outcome goals and behavioral goals are not made explicit
and no evidence is presented with regards to the effectiveness of
outcome goals or behavioral goals (or indeed other behavior
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1183
GOAL SETTING: META ANALYSIS
change techniques). One aim of the present research is to address
these limitations by examining the proposed distinction between
behavioral goals and outcome goals.
Correspondence between goal and action. According to
Ajzen and Fishbein’s (1977) principle of correspondence, the
effectiveness of a set goal on the dependent variable (i.e., behavior
or outcome) is likely to be related to how closely the set goal
resembles the measured behavior or outcome. Thus, goals that are
directly related to the dependent variable (e.g., a goal of complet-
ing five pages of arithmetic problems each day and a dependent
variable that measures the number of pages completed) are likely
to have larger effect sizes than goals and dependent variables that
are indirectly related (e.g., a goal of completing five pages of
arithmetic problems each day and a dependent variable that mea-
sures an arithmetic test score). Perhaps surprisingly, the effect of
correspondence between goal set and behavior/outcome has not
yet been tested.
Complementary behavior change techniques. With the ex-
ception of feedback (Chidester & Grigsby, 1984;Mento et al.,
1987;Tubbs, 1986), there has been little exploration of the effect
of pairing additional complementary behavior change techniques
with goal setting. It is clear that there are other behavior change
techniques that would increase the effectiveness of goal setting on
changing behavior. To self-regulate their behavior a person com-
pares their current state with a personally desired state (i.e., com-
mitment; BCTTv1 number 1.9) and if a discrepancy is detected
this motivates people to perform a behavior that reduces the
discrepancy (i.e., discrepancy between current behavior and goal;
BCTTv1 number 1.6). The process is iterative so feedback is
necessary in order to review progress (i.e., feedback on behavior
BCTTv1 number 2.2; feedback on outcomes of behavior BCTTv1
number 2.7). In addition, a person is more likely to choose a
behavior in order to pursue a goal if their expectations of success
are sufficient therefore reviewing goals to ensure they are achiev-
able could increase goal setting effectiveness (i.e., review behavior
goals BCTTv1 number 1.5; review outcome goals BCTTv1 num-
ber 1.7). Furthermore, witnessed behavioral contracts (BCTTv1
number 1.8) can only be signed once a goal has been set. The
effectiveness of these techniques to enhance the effect of goal
setting has thus far not been explored in previous reviews of goal
setting (with the exception of feedback), yet each might be ex-
pected to augment the effects of simply setting a goal.
Rationales
The present review will explore the tenets of goal setting theory
and extend the existing literature on goal setting by: (a) including
unpublished studies to address any publication bias; (b) including
only randomized controlled trials and excluding correlational stud-
ies; (c) including all behaviors, as opposed to focusing on specific
domains; (d) evaluating a more comprehensive list of moderators
(e.g., exploring the effects of goal setting for behavior vs. goal
setting for outcomes on behavior change); and (e) conducting a
secondary analysis to explore the potential additive effects of other
behavior change techniques that are intrinsically linked with goal
setting (i.e., commitment to the goal,
1
review of goals, behavioral
contract, discrepancies, and feedback).
Method
Selection of Studies and Inclusion Criteria
Studies were located using a search of four electronic databases
(Web of Knowledge, PsycINFO, PubMed, ProQuest Dissertation
Databases), using four search filters, and including all years until
November 4, 2015. The first filter, for goal setting and related
behavior change techniques (behavioral contract, commitment,
and review goals), used the search terms goal set
ⴱ
OR goal target
OR contract OR commitment OR goal review OR self standard.
The second filter was for study design to capture randomized
controlled trails: (random
ⴱ
AND intervention) OR (random
ⴱ
AND
experiment) OR (random
ⴱ
AND trial). The third filter referred to
dependent variables: goal OR behav
ⴱ
OR perform
ⴱ
OR outcome
OR consum.
ⴱ
The fourth filter was used to exclude “commitment
therapy.” To supplement the search of computerized literature
databases and obtain additional studies, the reference sections of
the selected articles were also examined along with the reference
sections of recent reviews and edited books of goal setting.
There were three inclusion criteria for the review. First, studies
had to test the unique effects of goal setting meaning that a
condition including goal setting had to be compared with a control
condition that was identical minus the goal setting component (for
a secondary analysis, articles were also included where the control
and intervention conditions differed in goal setting and one of the
related techniques of commitment, review of goals, discrepancies,
behavioral contract, or feedback). Second, the studies had to ran-
domize participants to condition. Third, the dependent variable
was behavior or outcome (i.e., an action depicted in the goal e.g.,
resisting unhealthy food or an outcome associated with such an
action e.g., weight loss)—studies that only measured variables
related to the goal setting process itself (e.g., satisfaction, ability to
form goals) were excluded.
Figure 1 shows the flow of articles throughout the review
(Moher, Liberati, Tetzlaff, & Altman, 2009). The literature search
identified 5,059 potentially relevant references and 133 references
were obtained from other sources (i.e., reference lists of included
articles, reviews of goal setting, recent edited books/chapters of
goal setting e.g., Locke & Latham, 2013). After eliminating du-
plicate references (n⫽628), the remaining 4,564 references were
screened for eligibility. Studies that did not meet the inclusion
criteria from the abstract (n⫽4,274) were excluded at this stage,
leaving 290 articles for which full texts were obtained and as-
sessed. Principal reasons for exclusion, at the abstract stage, were
that the article did not report a goal setting intervention (n⫽
2,218), was not an empirical study (n⫽1,742), and conditions
differed by a behavior change technique that was not goal setting
(or was not related to goal setting as per the secondary analysis;
n⫽246). Examination of the full texts led to the exclusion of a
further 111 articles. The principal reason for exclusion at this stage
was that conditions differed by a behavior change technique that
was not related to goal setting (n⫽56). The remaining articles
(n⫽141) met the inclusion criteria for the main analysis, reporting
384 tests of the impact of goal setting on behavior. A secondary
1
This is a conscious manipulation of goal commitment rather than the
particular level of commitment to which goal setting theory (Locke &
Latham, 2002) refers.
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1184 EPTON, CURRIE, AND ARMITAGE
analysis was conducted on articles that had included a behavior
change technique that complemented goal setting (n⫽38; 85
cases).
Study Coding
For studies that compared multiple goal setting conditions
with a control condition an effect size was calculated for each
experimental condition compared with the control condition
(with the nfor the control group adjusted for multiple compar-
isons; see Appendix S1 in the online supplemental materials).
For example, Bar-Eli, Levy-Kolker, Tenebaum, and Weinberg
(1993) compared a measurement-only control group with four
experimental groups who were asked to set “easy goals,” “mod-
erate goals,” “difficult goals,” and “very difficult goals.” Four
effect sizes were calculated using the control group (ndivided
by four) as the comparison.
Where studies did not report data separately for multiple goal
setting conditions the effect size was calculated so that it compared
the combined goal setting conditions with the control condition
(see Appendix S1). For example, Rosswork (1977) compared four
goal setting conditions with four control conditions and so for the
present analyses the effect size was calculated on the basis of all
four intervention conditions compared with all four control con-
ditions.
Where studies reported data from several subsamples, an effect
size was calculated for each subsample and the subsamples were
treated as separate tests in the meta-analysis to form the basis for
moderator analyses (see Appendix S1). For example, Schnoll and
Zimmerman (2001) compared two goal setting conditions (goal
setting with self-monitoring and goal setting alone) with two
similar control conditions; two tests were coded separately for the
meta-analysis to provide an effect size for goal setting only versus
control and an effect size for goals setting plus monitoring versus
monitoring only comparison group.
Where studies included multiple control/comparison groups the
effect size in the analysis was calculated using data from the
comparison group that most closely matched the goal setting
condition (see Appendix S1). For example, Nemeroff and Cosen-
tino (1979) compared three conditions: (1) goal setting and feed-
back, (2) feedback only, and (3) control; the feedback condition
was used as the comparison condition to the goal setting and
feedback condition.
Screening
Included Eligibility Idenficaon
Addional records idenfied
through other sources
(n=133)
Records excluded
(n = 4274)
• Not an empirical paper (n=1742)
• Not a goal seng intervenon (n=2218)
• Not a randomized controlled trial (n=15)
• Condions differ by non-goal seng behavior
change technique (n=246)
• Data reported elsewhere (n=7)
• Not appropriate outcome (n=3)
• Not an appropriate control (n=37)
• Not in English (n=3)
• Not available (n=3)
Full-text arcles excluded, with reasons
(n=111)
• Not an empirical study (n=3)
• Not a goal seng intervenon (n=22)
• Not a randomized controlled trial (n=14)
• Condions differ by non-goal seng behavior
change technique (n=56)
• Not an appropriate outcome (n=1)
• Not an appropriate control (n=13)
• Not in English (n=2)
Papers included in main
meta-analysis (n=141)
Papers included in goal
related behavior change
technique analysis (n=38)
Full-text arcles assessed
for eligibility
(n=290)
Records screened
(n=4564)
Records aer duplicates removed
(n=4564)
Records idenfied through
database searching
(n=5059)
Figure 1. Flow diagram of papers included in the meta-analysis.
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1185
GOAL SETTING: META ANALYSIS
Where studies measured behavior at multiple time points, we
adopted the conservative strategy of calculating the effect size only
for the measure at the final time-point (e.g., Epton et al., 2015;
Sheeran, Harris, & Epton, 2014). For example, Weinberg, Fowler,
Jackson, Bagnall, and Bruya (1991) measured sit ups once each
week over 4 weeks after the goal setting exercise manipulation; the
fourth week measure was used to calculate the effect size reported
in the present analyses.
If multiple behavioral dependent variables were reported, then
an effect size was calculated for each and a mean was used in the
analysis. For example, in Alexy’s (1985) study participants each
set a number of health goals (e.g., weight loss, seatbelt use) and so
an effect size was calculated for each type of goal and the mean
used in the present analyses.
Moderator Variables
Information about potential moderator variables was extracted
from each study (see Tables S2–S3 in online supplementary ma-
terials). The variables reported were chosen for their theoretical
importance or practical relevance and described features of: (a) the
sample, (b) the goal setting intervention, (c) other included behav-
ior change techniques, (d) the study design and delivery, (e)
quality of the study, (f) the behavior targeted, and (g) the level of
correspondence between the set goal and the dependent variable.
Samples were coded with respect to: (a) gender (percentage of
the sample that was female); (b) mean age; (c) ethnicity (percent-
age of the sample describing themselves as White, Black, His-
panic, Asian or other); and (d) the population from which the
sample was drawn (e.g., university students, general population,
schoolchildren).
The type of goal setting intervention was coded as: (a) behavior
(e.g., eat five pieces of fruit and vegetables per day) or outcome
(e.g., weight loss); (b) whether the basis of the goal was set relative
to the participant’s current standing (e.g., one more portion of fruit
per day) or to an external standard (e.g., eat five portions of fruit
and vegetables per day); (c) the number of different goals set; (d)
the number of times goal setting was repeated; (e) if training in
goal setting was provided; (f) the difficulty of the goal (i.e., easy,
moderate, difficult);
2
(g) task complexity;
3
and (h) whether com-
mitment was measured (rather than manipulated) in the experi-
mental group.
The interventions were assessed for: (a) the inclusion of behav-
ior change techniques in both the intervention and comparison
groups, and (b) the number of behavior change techniques used.
Features coded under study design and delivery were: (a) the
type of control group (i.e., measurement only/alternative interven-
tion, “do your best” goal); (b) how the goal setting exercise was
delivered (i.e., participant verbalizes, participant writes, other ver-
balizes, other writes); (c) the privacy of the goal setting (i.e.,
private, shared with intervention deliverer only, public); (d) if the
goal was an individual goal or group goal; (e) by whom the goal
was set (i.e., self, other, collaboratively); (f) if the timing of the
goal was proximal (i.e., was to be completed within a few of
weeks) or distal (was to be completed over a longer time period);
(g) if the goal was directed at one person but the behavior or
outcomes measured were for a different person (e.g., physiother-
apist set a goals for patient treatment and the patient outcomes
were measured); (h) who delivered the goal setting intervention
(i.e., researcher, clinician, instructor/teacher, was not delivered in
person); (i) the setting (i.e., university, worksite, school); and (j)
the interval between goal setting and outcome measurement.
Study quality was assessed using five indices: (1) publication
status, which was coded as peer reviewed (i.e., published or in
press articles) versus not peer reviewed (i.e., theses); (2) attrition
rate; (3) the randomization procedures; (4) blinding of participants
and investigators; and (5) treatment of participant attrition (Chal-
mers et al., 1990).
The dependent variables were also categorized as directly, in-
directly, or marginally related to the goal. The nature of the
targeted behavior was also assessed (e.g., health, educational, sport
performance, motor function, reaction time [RT], social, perfor-
mance on video game, cognitive, profit, production, environmen-
tal, keeping appointment, job related goals, negotiation).
Coding Reliability
All study characteristics were coded by Tracy Epton and Chris-
topher J. Armitage (both hold PhDs in health psychology). Inter-
coder reliabilities were calculated using Kappa (K) or intraclass
correlation (ICC), and were acceptable for both categorical (M
K
⫽
.983, range ⫽.887 to 1.00) and continuous variables (M
ICC
⫽
.989, range ⫽.860 to 1.00). Disagreements were resolved through
discussion between the authors.
Meta-Analytic Strategy
The effect size metric employed in the analyses was d. Means,
standard deviations, and Ns for the experimental versus the com-
parison condition were used to calculate the effect size whenever
possible (baseline values on behavior measures where controlled
for wherever possible). Where these statistics were not published
or provided by the authors, the effect size was calculated using
2
contingency tables for the experimental and comparison groups, or
derived from Fratios, tvalues,
2
values, or pvalues. If compar-
isons were reported as “statistically significant” without further
information, an effect size was calculated assuming p⫽.049. If
2
Goal difficulty was defined in terms of how far above “baseline” a goal
was set. “Baseline” could be derived from standard deviations of a pretest,
control group performance, previous studies, and pilot study scores. Where
this information was not available the percentage of people achieving the
goal in the goal setting group or in a pilot test was used. Goal difficulty was
coded using Kleingeld et al.’s (2011) system: “Difficult goals” were set at
least one standard deviation above baseline performance or less than 15%
of participants achieved the goal; “moderate goals” were set lower than one
standard deviation above baseline performance or 15% to 50% of partic-
ipants achieved the goal; and “easy goals” were set at zero or below
baseline performance or over 50% of people achieved the goal. Baseline
performance was ascertained from performance in control groups (n⫽74),
pretests/pilots (n⫽103), and previous studies (n⫽14). Where standard
deviation data was not available to calculate baseline performance the
percentage of people who attained the goal from performance in the goal
setting group (n⫽28), from previous studies (n⫽3) and from pilot studies
(n⫽12) was used. Where statistical data were not available qualitative
judgments reported in the text were used (n⫽9). Data, either for the exact
goal or the comparison, was not available for 134 cases.
3
Task complexity was scored based on the Wood et al. (1987) scale: RT
(1), brainstorming/simple maths/perceptual speed (2), toy assembly/ana-
grams/typing (3), sewing/production work/floor plan analysis (4), school
and college course work (5), supervision/middle management/technician
work (6), and science and engineering (7).
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1186 EPTON, CURRIE, AND ARMITAGE
comparisons were reported as nonsignificant or not mentioned as
significant these were treated in two ways: (a) nonsignificant
results were assumed to have an effect size of 0, and (b) an effect
size was calculated assuming p⫽.50.
A random effects model, weighted by sample size, was used to
calculate an overall effect size, plus 95% confidence intervals (CI),
significance of heterogeneity (Q), and the extent of heterogeneity
(I
2
) for the outcome variables, using the revised metan command
in STATA Version 11 (StataCorp, 2009). To explore the influence
of moderators, each outcome was regressed onto each potential
moderator using the revised metareg command in a random effects
model with restricted maximum likelihood estimation and the
improved variance estimator (Knapp & Hartung, 2003). The esti-
mated increase in the effect size per unit increase in the covariate
(regression coefficient ) and the percentage of heterogeneity
explained by the covariate (adjusted R
2
) were calculated.
Where subsets of studies were compared (e.g., easy, moderate,
and difficult goals) the effect sizes, and standard error of the effect
size for each subset were meta-analyzed (as above) and the Q
statistic examined—if there was significant heterogeneity then a
significant difference between the subsets was evident.
Results
Characteristics of Studies in the Main Analysis
The 141 articles that met the inclusion criteria for the main
meta-analysis included 155 studies (384 cases) for which effect
sizes were calculated (see Table S1 in the online supplementary
materials). The articles consisted of 128 (91%) published articles
and 13 theses (9%). The quality of the design of the studies/
reporting of the studies varied in ratings of blinding, randomiza-
tion, and attrition (Chalmers et al., 1990). The majority of cases
received low ratings on blinding and randomization; that is, 91%
were rated as “blinding was not possible or unclear if blinded” and
98% were rated as “randomized but method not described and
experimenter may not have been blinded to condition.” Ratings
regarding attrition and how the studies dealt with this varied, with
the majority of studies (67%) receiving the highest rating “attrition
did not occur or all those assigned to condition analyzed;” how-
ever, a large proportion of studies received a lower rating (31%) as
“attrition was not mentioned or completers only were analyzed.”
The cases varied in the type of populations sampled. Sixty-seven
percent recruited university students, 12% recruited the general
population, and 13% recruited schoolchildren. On average, the
samples were approximately equal regarding gender (49.50% fe-
male, SD ⫽24.09), of mostly White ethnicity (M⫽61.23%, SD ⫽
29.46) and were 23.71-years-old (SD ⫽11.01).
The goal setting interventions differed in the type of behavior
targeted; 49% were cognitive goals (e.g., complete maze in a
certain number of moves), 22% were sporting goals (e.g., perfor-
mance at archery), 7% were production goals (build Lego models),
6% were health related goals (e.g., weight loss), and 6% were
educational goals (e.g., increase study time). The majority of
studies asked participants to set goals related to outcomes (70%;
e.g., lose one pound of weight per week) as opposed to behaviors
(29%; e.g., eat five portions of fruit and vegetables daily), or both
(1%). The majority of goals were set in relation to external
standards (71%; e.g., do 50 sit-ups), as opposed to setting goals
relative to the participant’s current status (28%; e.g., improve
sit-up total by 20%) or both (1%). The mean number of goals set
was 1.35 (SD ⫽1.21), the average number of repetitions of setting
a goal was 3.82 (SD ⫽10.22) and training was given for goal
setting in 3% of the cases. The difficulty of goals varied: easy
goals (12%), moderately difficult goals (19%), and difficult goals
(33%). The mean task complexity was 3.58 (SD ⫽1.97).
The experimental and the comparison interventions included a
range of behavior change techniques other than goal setting that
were present in both the intervention and comparison groups.
These included behavioral practice or rehearsal (69%), feedback
on outcomes or behavior (40%), instruction on how to perform the
behavior (18%), self-monitoring of outcomes or behavior (12%),
demonstration of the behavior (10%), and monitoring of outcome
or behavior by others without feedback (6%). The mean number of
behavior change techniques was 1.74 (SD ⫽1.14).
The control groups were mainly “do your best” goals (i.e., a
vague goal; 66%), and measurement only/other intervention
(31%). A researcher (56%) typically delivered the goal setting
intervention, in a university setting (64%).
The goals were set by varied means; the main means were
experimenter verbalizes the goal (27%), participant writes goal
(14%), and written goal provided (22%). In a large number of
cases the goal was shared with the intervention deliverer only
(28%). The goals were mainly individual goals (88%) but also
included group goals (9%) or both (3%). Goals were typically set
by someone else (74%), but also included self set goals (18%),
collaboratively set goals (24%), and a combination of self and
other set (1%). The timing of the goals were mainly proximal
(81%), with some distal (15%) and few containing both kinds of
goals (4%). The mean follow-up period was 2.10 (SD ⫽5.36)
weeks after the goal setting intervention.
Impact of Goal Setting on Behavior
Across 384 tests (N⫽16,523) goal setting had statistically
significant effects on behavior (d⫽.34, CI [.28, .41]). According
to Cohen’s (1992) criterion the effect size is small (whereby d⫽
.20 is small and d⫽.50 is a medium sized effect). The analysis
was repeated with winsorized data to assess the effect of extreme
outliers (i.e., ⫾3SD; eight values were adjusted) and the effect
remained the same (d⫽.34, CI [.28, .39]). The analyses were
repeated: (a) excluding the studies that had not provided enough
data to calculate an accurate effect size (k⫽332, d⫽.40, CI [.33,
.47]); and (b) by replacing the estimates for nonsignificant results
with a value calculated from p⫽.50 (k⫽384, d⫽.34, CI[.28,
.41]).
The Fail Safe N, a calculation of the number of studies with an
effect size of zero that would be needed to make the effect
nonsignificant, is 269 (Orwin, 1983). An examination of the funnel
plots (see Figure S1) and the metabias command in STATA using
the Egger test were used to determine the presence of small study
effects; a significant skew was found, B ⫽.66, SE ⫽.27, p⫽.013.
The publication bias for small samples with significant results
leads to an excess of underpowered studies, leading to an overes-
timation of the effect size (Kraemer, Gardner, Brooks, & Yesav-
age, 1998). Therefore, sensitivity analysis using studies that meet
a 55% power threshold was conducted (recommended by Coyne,
Thombs, & Hagedoorn, 2010). For 55% power to detect a medium
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1187
GOAL SETTING: META ANALYSIS
effect size (d⫽.50) a sample of 35 or more participants per cell
was required (Epton et al., 2015). The effect of goal setting on
behavior remained significant (k⫽69, d⫽.38, CI [.26, .50])
when this criterion was used.
4
Moderator Analyses
The studies were significantly heterogeneous, Q⫽1199.05, p⬍
.001, and the variance attributable to heterogeneity was large, I
2
⫽
68.1% (see Table S2 and Table S3 in the online supplementary
materials for details of moderator analyses).
Sample. The studies included in the present review indicated
that goal setting was more effective in changing behavior when the
samples had a greater number of males (⫽⫺.01, p⫽.001, k⫽
268), were younger (⫽⫺.01, p⫽.006, k⫽123), had a greater
number of Asian participants (⫽.08, p⫽.005, k⫽13), and
were recruited from the general population (⫽.36, p⫽.001, k⫽
375 of which k⫽45 were general population), or were children
(⫽.27, p⫽.008, k⫽375 of which k⫽51 were children), and
were not university students (⫽⫺.25, p⫽.001, k⫽375 of
which k⫽258 were university students).
Goal setting intervention. The type of goal setting interven-
tion, that is, if outcome (k⫽383 of which k⫽271 were outcome)
or behavior (k⫽383 of which k⫽116 were behavior), based on
the participant’s current status (k⫽383 of which k⫽112 were
current status) or external standard (k⫽383 of which k⫽276
were external standard), number of different goals set (k⫽379),
number of times goal setting was repeated (k⫽378), and task
complexity (k⫽225) did not moderate the effect of goals setting
on behavior. There were too few studies (k⫽10) to analyze if
providing training in goal setting was a moderator.
Goal difficulty was a significant moderator (⫽.18, p⫽.005,
k⫽244) suggesting that increased goal difficulty leads to in-
creased goal success. Exploring the breakdown of easy, moderate,
and difficult goals; easy (k⫽45, d⫽.25, CI [.14, .37]) and
moderate goals (k⫽72, d⫽.25, CI [.17, .33]) had small effects
in contrast to the medium effect of difficult goals (k⫽127, d⫽
.45, CI [.39, .51]); there were significant differences between
difficult and easy goals, Q
b
⫽41.09, p⬍.001, and difficult and
moderate goals, Q
b
⫽21.33, p⬍.001.
Several studies included a measure of goal commitment that
participants completed after setting a goal but prior to performing
the task; measuring goal commitment in this way led to a smaller
effect of goal setting on behavior (⫽⫺.19, p⫽.030, k⫽384
of which k⫽74 measured commitment).
Additional behavior change techniques. There were suffi-
cient cases to explore the moderating effect of behavioral practice/
rehearsal (BCTTv1 number 8.1; k⫽384 of which k⫽265 used
rehearsal), feedback (BCTTv1 number 2.2 and 2.7; k⫽384 of
which k⫽154 used feedback), instruction on how to perform
behavior (BCTTv1 number 4.1; k⫽384 of which k⫽65 used
instruction), self-monitoring of behavior or outcome (BCTTv1
number 2.3 and 2.4; k⫽384 of which k⫽45 used self-
monitoring), demonstration of behavior (BCTTv1 number 6.1; k⫽
384 of which k⫽38 used demonstration), and social comparison
(BCTTv1 number 6.2; k⫽384 of which k⫽17 used social
comparison—there were no moderating effects of these variables
of goal setting on behavior. The only additional behavior change
technique (see Table S2) that increased the effect of goal setting
alone was monitoring of the behavior or outcomes by others
without feedback (BCTTv1 number 2.1 and 2.5; ⫽.60, p⬍
.001; k⫽384 of which k⫽22 used monitoring without feedback).
Other behavior change techniques that were used alongside goal
setting but used too infrequently to analyze were information about
antecedents (BCTTv1 number 4.2, k⫽9), rewards (BCTTv1
numbers 10.2, 10.3, and 14.5, k⫽7), prompts/cues (BCTTv1
number 7.1, k⫽7), social support (BCTTv1 numbers 3.1, 3.2, and
3.3, k⫽7), incentives (BCTTv1 numbers 10.1 and 10.6, k⫽7),
information about health consequences (BCTTv1 number 5.1, k⫽
4), salience of consequences (BCTTv1 number 5.2, k⫽4), prob-
lem solving (BCTTv1 number 1.2, k⫽4), valued self-identity
(BCTTv1 number 13.4, k⫽3), add object to environment
(BCTTv1 number 12.5, k⫽3), action planning (BCTTv1 number
1.4, k⫽1), and self-talk (BCTTv1 number 15.4, k⫽1).
Study design and delivery. There were no differences in
effect size with regards to “do your best” comparison groups (k⫽
384 of which k⫽253 had “do your best” comparison) or mea-
surement only/alternative intervention comparison groups (k⫽
384 of which k⫽120 used measurement only/alternative inter-
vention comparison). There were no differences in behavior due to
the means with which the goal was set for other verbalizing the
goal (k⫽267 of which k⫽103 had another person verbalizing the
goal), other writing the goal (k⫽267 of which k⫽87 had another
person writing the goal), and participant writing the goal (k⫽267
of which k⫽54 had the participant writing the goal). There were
no differences if the goal was proximal (k⫽380 of which k⫽324
were proximal) or distal (k⫽380 of which k⫽70 were distal).
There were no differences due to the time interval between the
goal setting and the measurement of the dependent variable (k⫽
378). There were too few cases to analyze differences due to if the
goal was made by the person whose behavior or outcomes were
measured as opposed to if the goal setting was directed at a person
whose behavior was not measured (e.g., physiotherapist set a goal
for how to treat a patient but the behavior measured was patient
outcomes).
The present dataset showed that goal setting interventions were
more effective if the goal: (a) was set publicly (⫽.41, p⬍.001,
k⫽255 of which k⫽58 were public); (b) was a group goal (⫽
.48, p⬍.001, k⫽384 of which k⫽48 were group goals) rather
than an individual goal (⫽⫺.29, p⫽.018, k⫽384 of which
k⫽349 were individual goals); (c) was set face-to-face (rather
than online or computerized; ⫽⫺.46, p⫽.003, k⫽276 of
which k⫽20 were set online/computerized); and (d) was set in a
workplace (⫽.44, p⬍.001, k⫽345 of which k⫽30 were set
in a workplace), or school (⫽.23, p⫽.021, k⫽345 of which
k⫽47 were set in a school) and not a university (⫽⫺.23, p⫽
.003, k⫽345 of which k⫽247 were set in a university).
4
To aid the moderator analyses some studies had been broken down into
separate cases for example, males and females. However, as this led to
many cases with relatively low Ns these cases were recombined (i.e., a
mean of the effect size for the males and females was calculated and
included as one case) for this subanalysis to maximize the number of cases
that achieved 55% power (i.e., had n⫽35 per condition). Cases from the
same studies, that had been treated as separate cases to enable the main
analysis to explore moderators, were combined for this subanalysis to
increase the number of studies that achieved 55% power.
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1188 EPTON, CURRIE, AND ARMITAGE
Study quality. Study quality did not moderate the effect of
goal setting on behavior; there were no moderating effects of
randomization rating (k⫽384), blind rating (k⫽384), attrition
rating (k⫽384), actual attrition (k⫽288), whether or not the
study was peer reviewed (k⫽384 of which k⫽346 were peer
reviewed), or date of study (k⫽384).
Type of behavior. The largest effect sizes were for environ-
mental goals (e.g., increasing recycling; k⫽2, d⫽.57, CI [.07,
1.07]), health (k⫽21, d⫽.44, CI [.31, .56]), sporting goals (k⫽
83, d⫽.41, CI [.33, .49]), production goals (e.g., building Lego
models; k⫽25, d⫽.36, CI [.19, .52]), education goals (k⫽21,
d⫽.30, CI [.16, .44]), and cognitive goals (k⫽188, d⫽.28, CI
[.23, .32]). Other significant effects were found for keeping ap-
pointments (k⫽2, d⫽.26, CI [.03, .49]; see Table S3).
Correspondence between goal and dependent variables.
The effect size differed with regards to how closely the goal that
was set corresponded with the outcome. Direct goals (e.g., goal set
was regarding weight loss and outcome was weight loss; k⫽337,
d⫽.37, CI [.31, .44]) had a greater effect on behavior than when
the outcome measure differed from the goal that was set (e.g., the
goal set was regarding resisting food and outcome was weight loss;
k⫽99, d⫽.18, CI [.05, .31]) and when the measure was
marginally related to the goal (k⫽5, d⫽.10, CI [⫺.21, .40]),
Q
b
⫽29.73, p⬍.001.
Secondary Analysis of Studies That Used Behavior
Change Techniques Complementary to Goal Setting
Across 27 tests (N⫽5,751) that had combined goal setting with
commitment (BCTTv1 number 1.9) there were significant effects
on behavior (d⫽.20, CI [.14, .26]); however, this was signifi-
cantly lower than the effect of the studies with goal setting alone
(d⫽.34, CI [.28, .41]), Q
b
⫽15.08, p⬍.001.
Studies that paired feedback and goal setting (BCTTv1 numbers
2.2 and 2.7) were not effective compared with the control condi-
tions (k⫽25, N⫽1071, d⫽.01, CI [⫺.27, .29]). Studies that
paired goal setting with a review of behavior or outcome goals
(i.e., discuss and consider modifying goals in light of progress or
lack of progress; BCTTv1 numbers 1.5 and 1.7) were not effective
in comparison to the control conditions (k⫽12, N⫽343, d⫽.17,
CI [⫺.05, .40]). Studies that paired goal setting with a behavioral
contract (i.e., a written specification of the goal that is witnessed
by another; BCTTv1 number 1.8) were no more effective than the
control condition (k⫽21, N⫽791, d⫽.11, CI [⫺.09, .30]).
However, it should be noted that the sample sizes for these
analyses were small. There were no studies that combined a goal
setting intervention with discrepancies (i.e., drawing a discrepancy
between current behavior and a previously set goal).
Discussion
Encouraging people to set goals is a technique that is used
widely to promote behavior change, and the present review
showed a positive effect on behavior. Relative to participants in
comparison conditions, participants in goal setting conditions
showed greater behavior change across a wide range of behaviors.
Various analyses and tests indicate that the effect is robust. The
small sized effect (d⫽.34) was retained when: (a) extreme
outliers were dealt with (d⫽.34); and (b) estimates based on an
effect size of zero were replaced with values calculated from p⫽
.50 (d⫽.34). The small sized effect was marginally increased
when: (a) studies were excluded that had been estimated based on
an effect size of zero (d⫽.40); and (b) studies were excluded that
did not reach a stated power threshold (d⫽.38). Publication bias
was addressed by including papers from unpublished sources,
although the Egger test did show that there was skewness. The
overall effect found in the present review is smaller than the
medium effect sizes of previous reviews (the overall effect sizes
found by the previous meta-analyses range from d⫽.42 to d⫽
.56; Chidester & Grigsby, 1984;Kleingeld et al., 2011;Mento,
Steele, & Karren, 1987;Tubbs, 1986;Wood, Mento, & Locke,
1987). However, the present review is arguably more robust be-
cause it excluded correlational studies, included unpublished dis-
sertations and studies that had reported a nonsignificant result but
did not provide data.
Although the effect of goal setting on behavior is small, it is
comparable with reviews of other individual behavior change
techniques such as those that reflect on a valued self-identity (d⫽
.32; Epton et al., 2015; BCTTv1 number 13.4) and action planning
(d⫽.31; Belanger-Gravel, Godin, & Amireault, 2013; BCTTv1
number 1.4). The implication is that goal setting is one of the
building blocks for designing effective behavior change interven-
tions. However, behavior change techniques that we identified as
complementary to goal setting, namely, feedback, commitment,
behavioral contracts, and reviewing goals seemed to add little to
the effect of goal setting per se. These findings contradict predic-
tions made by goal setting theory (Locke & Latham, 2002) that
feedback, commitment, reviewing goals and behavioral contracts
would increase the effectiveness of goal setting. However, this
lack of effect may be due to the small number of studies included
in this analysis (it should be noted that review of behavior or
outcome goals had a small sample size of k⫽12, that behavioral
contract and feedback had modest sample sizes of k⫽21 and k⫽
25, respectively). It is noteworthy that a comparison of current
standing and the goal (i.e., discrepancy) was not used in the studies
included in this analysis or at least not explicitly detailed.
Theoretical Moderators
Goal setting theory (Locke & Latham, 2002,2006) postulates
that goals are optimally effective if: (a) the goal is sufficiently
difficult, (b) people are committed to the goal, (c) the task com-
plexity is not too high, (d) feedback on goal progress is provided,
and (e) there are adequate situation resources/few situational con-
straints.
Difficulty. The studies in the present review provided evi-
dence that goal difficulty (i.e., the extent to which the goal that was
set exceeded that which would be typically achieved) moderated
the effect of goal setting on outcomes with more difficult goals
having a stronger effect than easier goals. The analysis showed that
easy and moderate goals were effective but only had a small effect
compared to the larger effect of difficult goals; this is comparable
with what had been found in previous reviews and is consistent
with goal setting theory (Locke & Latham, 2002,2006). However,
it is important to note that improbable difficult goals may be
detrimental; most goals coded as difficult in this meta-analysis
were still achievable (k⫽10 were coded as improbable so it was
not possible to analyze the effect of this). It is important to note
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1189
GOAL SETTING: META ANALYSIS
that goals should still be set within an achievable range for that
person, as although improbable goals may still have a positive
influence on motivation (Weinberg, Bruya, Garland, & Jackson,
1990), they may reduce achievement (Bar-Eli et al., 1997).
Commitment. Manipulating goal commitment was associated
with a small but significant effect; however, the size of this effect
(d⫽.20; k⫽27) was significantly lower than the effect of those
studies that used goal setting alone (d⫽.34). Goal theory (Locke
& Latham, 1990,2002,2006) states that goal commitment is a
necessary prerequisite for goal attainment when the goals are
difficult. There were too few studies to explore the effect of
manipulating goal commitment only when goal difficulty was high
(k⫽1). However, this result suggests that manipulating goal
commitment has a negative effect on goal progress when the goals
are easy or moderate.
Furthermore, studies that measured goal commitment (after
setting the goal but prior to the goal setting task) resulted in less
positive outcomes than those studies that did not ask participants
about their commitment (k⫽74). This negative effect may be due
to participants recognizing that they are not committed to the goal,
and thus spend little effort on pursuing the goal. However, there
are debates around the measurement of goal commitment even
using established scales, as these scales included items that reflect
outcome expectancies and items that lower the scale consistency
(Klein, Wesson, Hollenbeck, Wright, & DeShon, 2001); as most
studies were conducted prior to new scale development it is not
possible to explore the effect of measurement errors.
Task complexity. The present meta-analysis suggests that
task complexity does not moderate the effectiveness of goal at-
tainment. This mirrors the results of the meta-analysis of Klein-
geld, van Mierlo, and Arends (2011) who also found no effect but
contrasts with the meta-analysis of Wood et al. (1987) who found
that increased complexity reduced performance. Kleingeld et al.
(2011) suggests three reasons why a significant effect of task
complexity was not found in their meta-analysis: (a) that the
groups’ goal setting in their meta-analysis showed superior ability
to overcome the problems with task complexity than the individ-
uals in Wood et al.’s (1987) review, (b) that only six studies used
a highly complex task in their analysis so may not be reliable, and
(c) the moderating effect in Wood et al.’s (1987) meta-analysis
may have been overstated as the difference between the effect
sizes for low, moderate and high complexity was only small. The
present meta-analysis included studies that mainly sampled uni-
versity students who would be expected to have sufficient skills
and knowledge to conduct complex tasks; however, the review
also found that goal setting was less effective in student samples
suggesting that high ability might not be the cause of a lack of a
moderating effect of task complexity.
Given that the present review has a modest but larger number of
studies (k⫽64) with a high task complexity, than Kleingeld et al.
(2011) and Wood et al. (1987), this suggests that either (a) the
analysis in the present study may also not be reliable or (b) the
moderating effect of Wood et al.’s (1987) meta-analysis may have
been overstated. It is notable that Wood et al. (1987) did not
conduct a statistical test of the differences between levels of task
complexity. The results of the present review (d⫽.26, k⫽107 for
low; d⫽.22, k⫽54 for medium; d⫽.22, k⫽64 for high) show
that the effects of low, medium, and high task complexity were
much smaller than those of Kleingeld et al. (2011;d⫽.32 to .56
for low, d⫽.48 to .87 for medium, d⫽.33 to 1.08 for high) and
Wood et al. (1987;d⫽.69 for low, d⫽.50 for medium, d⫽.48
for high); however, this could be due to the inclusion of studies in
the present analyses that reported a nonsignificant results with no
data to calculate an effect size. An additional factor that may
account for the mixed results is the fact that task complexity has
typically been operationalized objectively (i.e., the number of task
components) but may include subjective components such as the
capability of the individual to perform the task. Indeed, later
versions of goal setting theory suggest that if the complexity of a
task exceeds the person’s ability then a learning goal rather than a
performance goal should be set (Locke & Latham, 2006).
Feedback. The present analyses did not add support to the
claim of goal setting theory that feedback increases the effective-
ness of goal setting as including feedback (k⫽25) was not
associated with a significant effect on behavior and outcomes;
however, the sample size was moderate so strong conclusions
cannot be drawn.
Situational resources/constraints. The review found no
studies that had met the inclusion criteria that had explicitly
manipulated resources or constraints. Moreover, there was not
enough detail reported in the studies to develop a coding frame to
explore this variable. It would be valuable in future research to
investigate empirically the effects of situational resources/con-
straints on the effects of goal setting.
Other Moderators
Sample. The studies in the present review indicated that goal
setting was particularly effective for males, younger participants,
and samples from the general population or children recruited from
schools. It is notable that effect sizes for behavior change were
smaller among student samples; given that 67% of the studies
reported in the present analyses were carried out with university
student participants, the possibility arises that goal setting-based
interventions could be particularly effective outside this narrow
population base. Moreover, goal setting was most effective for
Asian participants, although the analyses should be interpreted
with caution due to the small number of studies containing this
information. Nevertheless, it would be valuable in future research
to investigate further the effects of goal setting on a wider demo-
graphic outside of the undergraduate student body.
Type of goal setting intervention. The data in the present
review suggest that goal setting was equally effective irrespective
of: whether the basis of the goal was participant’s current standing
or external standard; the number of different goals set; or the
number of times the goal setting was repeated. Moreover, there
were no differences in goal attainment dependent upon whether the
goal targeted behavior or outcomes— goals regarding behavior and
goals regarding outcomes both had small but significant effects on
behavior. Our moderator analyses do not provide strong support
for a distinction between setting behavioral goals versus setting
outcome goals: Further analysis shows that setting behavioral
goals had slightly stronger effects on behavior than on outcomes;
setting outcome goals had slightly larger effects on outcomes than
on behaviors. That said, our data are correlational and no studies to
date have directly assessed the effects of setting behavioral goals
versus setting outcome goals on behaviors versus outcomes.
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1190 EPTON, CURRIE, AND ARMITAGE
Behavior change techniques. Monitoring of behavior or out-
come by others without feedback improved the effect of goal
setting over and above an intervention that monitored behavior/
outcome but without goal setting; however, this was based on a
modest sample size (k⫽22). Interestingly, goal setting was just as
effective when it was used in isolation than when it was combined
with all the other behavior change techniques that have thus far
been tested when compared with a control group that also included
that technique. A relatively large number of studies were included
for several behavior change techniques (i.e., behavioral practice/
rehearsal, feedback, instruction on how to perform the behavior,
self-monitoring of behavior or outcome, and demonstration of the
behavior) suggesting that these do not augment the effectiveness of
goal setting when compared with a comparison group that just
used these behavior change techniques without goal setting.
Fifty-six out of the 93 techniques in the BCTTv1 have not yet
been combined with goal setting. Other behavior change tech-
niques that are successful in changing behavior, such as providing
a reward if progress toward the goal is made (BCTTv1 numbers
10.1 to 10.10), may further augment the effect of goal setting
(Shilts et al., 2013).
Study design and delivery. The present meta-analysis indi-
cates that optimal goal setting interventions should be: (a) set
publicly, (b) set by someone else, and (c) set for a group. There is
also evidence that goal setting interventions should be set face to
face rather than online or computerized; however, the number of
studies that did not set goals face to face was modest. These results
seem to suggest that an actual or inferred social presence maxi-
mizes the effect of goal setting.
The studies included in the present review show clear evidence
that there are no differences dependent upon if the goals were set
by someone else, self-set or participatively set goals. This is in
contrast to: (a) predictions made by theories such as self-
determination theory that suggests the autonomy from self set
goals should increase motivation and thus improve behavior
change (Deci & Ryan, 2000); and (b) recommendations that col-
laborative goals should be used in clinical settings (Matre et al.,
2013;Shilts et al., 2013). The results of the present review may be
attributable to the participants making goals of insufficient quality
when self-setting or collaboratively setting goals in the studies
included in this review; furthermore, the goal interventions may
have not allowed the time necessary for forming effective collab-
oratively set goals (e.g., guided goal setting; Shilts et al., 2013). It
is notable that instruction in goal setting (only 10 studies provided
training in goal setting).
Goal setting is also more effective if set in a school or workplace
(rather than a university). Coupled with the moderating effect of
sample type (i.e., that goal setting is more effective for schoolchil-
dren and the general population and less effective in university
students), the findings suggest that there may be differences be-
tween studies set in universities with students and other studies.
This could be due to the type of behavior targeted as there were
substantially fewer studies targeting health and education (that had
strong effects) that used university students.
Correspondence between goal and dependent variable.
The level of correspondence between the goal and the dependent
variable was a significant moderator. Goals that were directly
related to the dependent variable had a significantly larger effect
size than those that were more indirectly related. This finding is
consistent with Ajzen and Fishbein’s (1977) principle of corre-
spondence, which is normally associated with attitude-behavior
relations but clearly applies equally to relations between goal
setting and behavior change.
Type of behavior. Goal setting was shown to be effective for
a range of behaviors; in particular health, sport, production tasks,
education, and cognitive tasks. Goal setting has been used rarely
for other tasks such as social and environmental issues so conclu-
sions cannot be drawn about the effectiveness of goal setting for
these behaviors. Nevertheless, the broad applicability of goal set-
ting implies that it may constitute a key building block of success-
ful behavior change interventions.
Complementary Goal Setting Behavior
Change Techniques
The addition of behavior change techniques proposed by the
BCTTv1 (Michie et al., 2013) that we hypothesized as enhanc-
ing the effects of goal setting, namely, behavioral contract and
reviewing outcome or behavioral goals were ineffective. This
suggests that goal setting is an effective and robust behavior
change technique in and of itself; however, due to the modest
sample sizes more studies are needed to provide a more con-
clusive answer.
Research Implications
The review highlights numerous important areas for further
research into goal setting that have hitherto received relatively
little attention. We would like to draw particular attention to six
areas that we believe would benefit from further research. First, it
is notable that observations of the unique effects of goal setting on
behavior have been restricted to the short–medium term: No stud-
ies eligible for inclusion in the present review have yet established
whether the effects of goal setting are sustained beyond 12 months
postintervention. Second, the unique effects of goal setting have
not yet been fully tested in several important behavioral domains
(e.g., environmental behavior), among key target populations (e.g.,
low socioeconomic status), and in key contexts (e.g., primary
care). Third, the behavior change technique taxonomy (Michie et
al., 2013) and the broader goal setting literature (e.g., Kim, 1984)
describe a potentially important distinction between setting goals
to achieve outcomes (e.g., weight loss) versus setting goals to
achieve behaviors (e.g., increased physical activity), yet there is
little experimental research that explores this distinction. Fourth, it
is not clear how people should be encouraged to set goals, for
example, whether it is sufficient for people to be given a goal or
whether they should be trained to set goals and what roles new
technologies have to play in helping people to set goals. Fifth, 56
out of the 93 behavior change techniques identified to date (Michie
et al., 2013) have not yet been paired with goal setting and it is
plausible that lack of power might account for at least some of the
null effects reported in the present meta-analysis. Sixth, mediators
of goal setting have been proposed by goal setting theories (e.g.,
Locke & Latham, 2002), however, very few studies report the
relationship between the mechanisms of action and changes in
behavior.
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1191
GOAL SETTING: META ANALYSIS
Conclusion
The present meta-analysis provides an examination of the
unique effects of goal setting on behavior change across a variety
of domains, populations, and contexts. Goal setting was shown to
exert small but robust effects on behavior change. Moreover, the
analyses support the two central tenets of goal setting theory
(Locke & Latham, 2002,2006), namely, that setting specific and
difficult goals are effective at increasing behavior change. How-
ever, there was no evidence to support the hypotheses that task
complexity, feedback, and commitment boost the effects of goal
setting. Instead, the present findings show that for the studies
included in this review goal setting is optimally effective when: (a)
it is set face-to-face, (b) it is set publicly, (c) it is a group goal, and
(d) it is coupled with monitoring of the behavior or outcome by
another person without feedback. Further primary research is re-
quired to see whether goal setting theory (Locke & Latham, 2002,
2006) needs updating and to ascertain whether as-yet untested
behavior change techniques can complement the effects of goal
setting on behavior change.
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Received October 18, 2016
Revision received September 5, 2017
Accepted September 7, 2017 䡲
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