2011, 64, 489–528
A META-ANALYTIC EXAMINATION OF THE
INSTRUCTIONAL EFFECTIVENESS OF
COMPUTER-BASED SIMULATION GAMES
University of Colorado Denver
Interactive cognitive complexity theory suggests that simulation games
are more effective than other instructional methods because they simul-
taneously engage trainees’ affective and cognitive processes (Tennyson
& Jorczak, 2008). Meta-analytic techniques were used to examine the
instructional effectiveness of computer-based simulation games relative
to a comparison group (k=65, N=6,476). Consistent with theory, post-
training self-efﬁcacy was 20% higher, declarative knowledge was 11%
higher, procedural knowledge was 14% higher, and retention was 9%
higher for trainees taught with simulation games, relative to a compari-
son group. However, the results provide strong evidence of publication
bias in simulation games research. Characteristics of simulation games
and the instructional context also moderated the effectiveness of sim-
ulation games. Trainees learned more, relative to a comparison group,
when simulation games conveyed course material actively rather than
passively, trainees could access the simulation game as many times as
desired, and the simulation game was a supplement to other instructional
methods rather than stand-alone instruction. However, trainees learned
less from simulation games than comparison instructional methods when
the instruction the comparison group received as a substitute for the sim-
ulation game actively engaged them in the learning experience.
Video games such as Pac-Man (developed by Namco, 1980) and Pong
(manufactured by Atari Corporation, 1972) are icons of popular culture
from the late 20th century. These video games utilized simplistic graph-
ics and entertained millions of players. As video games increased in
popularity, game developers realized the potential of capitalizing on the
entertainment value of games and teaching instructional content during
game play in order to advance into the education arena. As a result, pop-
ular computer games such as Where in the World is Carmen Sandiego?
(produced by Brøderbund Software in 1985) and Oregon Trail (produced
by MECC in 1974) were developed to teach geography and life on the
I would like to thank Katherine Ely for her assistance coding reports included in the
meta-analysis and commenting on an earlier version of the manuscript.
Correspondence and requests for reprints should be addressed to Traci Sitzmann, Univer-
sity of Colorado Denver, School of Business, PO Box 173364, Campus Box 165, Denver,
CO 80217-3364; Traci.Sitzmann@ucdenver.edu.
C2011 Wiley Periodicals, Inc.
490 PERSONNEL PSYCHOLOGY
American frontier (CNET Networks Entertainment, 2004). Recently, the
term “serious games” was coined to refer to simulation games designed
to address more complicated and thought-provoking issues such as geno-
cide, military combat, civil court procedures, and training ﬁrst responders
Simulation games refer to instruction delivered via personal com-
puter that immerses trainees in a decision-making exercise in an artiﬁcial
environment in order to learn the consequences of their decisions. Simula-
tion games are intrinsically motivating (Malone, 1981), and people report
experiencing a loss of time when playing their favorite games (Wood,
Grifﬁths, & Parke, 2007). Furthermore, they are widely popular in the
United States—approximately 40% of adults play video games (Slagle,
2006). The ultimate goal for training professionals is to harness the mo-
tivational capacity of video games to enhance employees’ work-related
knowledge and skills (Garris, Ahlers, & Driskell, 2002).
The goal of this study is to statistically summarize the literature on
the instructional effectiveness of computer-based simulation games for
teaching work-related knowledge and skills. This will provide insight as to
whether game play is effective for improving adults’ work competencies.
Furthermore, it will clarify the characteristics of simulation games that
are essential for maximizing learning outcomes.
Empirical and anecdotal evidence suggest that computer-based simu-
lation games are effective for enhancing employees’ skill sets. Cold Stone
Creamery developed a simulation game to teach customer service and por-
tion control in a virtual Cold Stone store (Jana, 2006). Players race against
the clock to serve customers in a timely fashion while maximizing the com-
pany’s proﬁt by avoiding wasting too much ice cream. The ﬁrst week the
simulation game was available more than 8,000 employees—representing
30% of the workforce—voluntarily downloaded the simulation game. Cor-
porate trainers believe that the entertainment value will incline employees
to continuously play the simulation game while simultaneously teaching
them retail sales, technical, and managerial skills. Canon Inc. utilizes a
simulation game to teach copy machine repairs. Players must drag and
drop parts into the right spot on a copier and, similar to the board game
Operation, a light ﬂashes and a buzzer sounds when a mistake is made.
Employees who played the game obtained training assessment scores 5%
to 8% higher than those trained with older techniques, such as manuals
In 2003, the corporate simulation-based training industry was spending
between $623 and $712 million globally (Summers, 2004). However, there
is dissention among researchers and practitioners on the effectiveness
of simulation games (O’Neil, Wainess, & Baker, 2005; Prensky, 2001;
Randel, Morris, Wetzel, & Whitehill, 1992; Tobias & Fletcher, 2007;
TRACI SITZMANN 491
Vogel et al., 2006). Others have noted that there are not clear guidelines
on the features of simulation games that enhance learning (Bell, Kanar, &
Kozlowski, 2008; Federation of American Scientists, 2006; Garris et al.,
2002; Tennyson & Jorczak, 2008).
The goal of this study is to address the debate regarding whether
simulation games enhance work-related knowledge and skills as well as
to determine which simulation game design features enhance learning. I
conducted a meta-analysis of 65 independent samples and data from more
than 6,000 trainees in order to examine the instructional effectiveness
of computer-based simulation games. Trainees taught with simulation
games were evaluated against a comparison group on key affective and
cognitive training outcomes. The comparison group differed across studies
and ranged from a no-training control condition to trainees who received
alternative instructional methods as a substitute for the simulation game.
I also examined trends across studies to determine if the effectiveness
of simulation games, relative to the comparison group, differed based on
features of simulation games and the comparison group, characteristics of
the instructional context, and methodological factors.
There have been three previous quantitative reviews of the effective-
ness of simulation games (Lee, 1999; Randel et al., 1992; Vogel et al.,
2006). This meta-analysis expands on these reviews in several ways. First,
they all utilized a combination of child and adult samples, precluding a
precise estimate of the effectiveness of this instructional method for teach-
ing adults work-related skills. Second, Randel et al. focused strictly on
cognitive learning outcomes, whereas Lee and Vogel et al. examined both
cognitive learning and attitudes toward training. This meta-analysis exam-
ined several training outcomes (i.e., self-efﬁcacy, declarative knowledge,
procedural knowledge, and retention) and expanded on the moderators
that have been examined—including both theoretical and methodologi-
cal moderators. Speciﬁcally, ﬁve theoretical moderators (entertainment
value, simulation game instruction was active or passive, unlimited ac-
cess to the simulation game, whether the simulation game was the sole
instructional method, and whether the instructional methods used to teach
the comparison group were active or passive) and four methodological
moderators (random assignment to experimental conditions, rigor of the
study design, publication status, and year of the publication, dissertation,
or presentation) were examined. Third, the most recent studies in previous
reviews were published in 2003. This review includes studies from as far
back as 1976 to as recent as 2009 in order to examine whether the instruc-
tional effectiveness of simulation games has evolved over time. Fourth,
Vogel et al. and Lee included one and two unpublished studies in their
reviews, respectively. Sixteen of the studies in this review were unpub-
lished, permitting an examination of whether there is publication bias in
492 PERSONNEL PSYCHOLOGY
this line of research. Thus, the goal of this investigation was to provide
a considerably more comprehensive, quantitative review of the effects of
simulation games on training outcomes. Accurate effect size estimates are
imperative for both researchers and practitioners in that they provide a
basis for comparing the effects of alternative instructional methods, for
conducting power analyses in future research, and for estimating training
utility. In the following section, I review deﬁnitions of games and simula-
tions in order to alleviate some of the confusion that the interchangeable
use of these terms has generated. I then review several motivation and
learning theories in order to provide an integrated theoretical framework
for understanding the instructional effectiveness of simulation games.
Deﬁnition of Simulation Games
The simulation game literature is plagued by an abundance of deﬁni-
tions and little consensus on the deﬁning features of instructional simu-
lations and games (Garris et al., 2002; Hays, 2005; O’Neil et al., 2005).
Several popular deﬁnitions of games agree that they are entertaining, in-
teractive, rule-governed, goal-focused, competitive, and they stimulate the
imagination of players (Driskell & Dwyer, 1984; Gredler, 1996; Tobias &
Fletcher, 2007; Vogel et al., 2006). The distinguishing feature of simula-
tions is that they are reality based, but they can also incorporate common
game features such as rules and competition (Bell et al., 2008; Hays, 2005;
Tobias & Fletcher, 2007).
Consistent with Tennyson and Jorczak (2008), I propose that there
are no longer clear boundaries between these two instructional methods.
For example, Ricci, Salas, and Cannon-Bowers (1996) researched the ef-
fectiveness of a game for teaching chemical, biological, and radiological
defense. This game involved answering a series of multiple-choice ques-
tions, but it provided very little entertainment value, which is a key feature
of games in most deﬁnitions. In addition, simulations are proposed to be
based on reality, but the literature is rich with examples of computer-based
simulations that do not faithfully recreate work-related experiences (e.g.,
North, Sessum, & Zakalev, 2003; Shute & Glaser, 1990; Taylor & Chi,
2006). Given the blurred boundaries, it is not valuable to categorize these
educational tools as either simulations or games; instead, I use the broad
term simulation games. Consistent with previous deﬁnitions (e.g., Siemer
& Angelides, 1995; Tennyson & Jorczak, 2008), I deﬁne computer-based
simulation games as instruction delivered via personal computer that im-
merses trainees in a decision-making exercise in an artiﬁcial environment
in order to learn the consequences of their decisions. Thus, learning must
be the primary goal of the simulation game in order to be included in
this research. Furthermore, online training is distinct from simulation
TRACI SITZMANN 493
games—online training is always delivered via the Internet, whereas sim-
ulation games may be online or hosted on a single workstation computer;
online training also utilizes a breadth of instructional methods (e.g., lec-
ture, assignments, discussion), one of which may be simulation games.
Theoretical Framework for Simulation Games
Several theories have been proposed for understanding the role of
simulation games in enhancing the motivation and learning of trainees.
The earliest theories focused exclusively on the motivational potential
of simulation games, ignoring their potential for enhancing work-related
skills. Consistent with cognitive-oriented learning theories (Bruner, 1962;
Piaget, 1951), Malone’s (1981) theory emphasized the importance of
intrinsically motivating, play-type activities for promoting deep learning.
When trainees are intrinsically motivated, they exert more effort to learn
the material, enjoy learning more, and are more likely to apply the material
outside of the game environment.
Garris et al.’s (2002) input–process–outcome model also focused on
the motivational capacity of simulation games. They proposed that in-
structional content and game characteristics serve as input to the game
cycle (the process), which ultimately inﬂuences learning outcomes. The
game cycle is the motivational aspect of the model. It represents a cycli-
cal relationship among user judgments (i.e., enjoyment, task involvement,
and self-efﬁcacy), user behavior (i.e., effort expended and the decision to
continue playing), and system feedback (i.e., information about one’s per-
formance). Simulation games should be designed to be engaging and en-
grossing to perpetuate the game cycle. This creates a ﬂow state that repre-
sents an optimal performance situation in which trainees are so involved in
the simulation game that nothing else seems to matter (Csikszentmihalyi,
1990). After one or more game cycles, trainees should participate in a
debrieﬁng session in which the simulation game as well as its applica-
bility to the real world is discussed. Debrieﬁng enhances the transfer of
what trainees have learned in the simulation game to the job. The beneﬁts
of this model are that it captures the process by which simulation games
motivate trainees and engage them in game play and demonstrates the
essential role of debrieﬁng in enhancing transfer from game play to the
Tennyson and Jorczak (2008) pushed simulation game theory a step
further by focusing on the cognitive systems of trainees that affect learn-
ing. Interactive cognitive complexity is an integrative information pro-
cessing model that proposes learning is the result of an interaction be-
tween variables internal and external to the cognitive systems of trainees.
Trainees’ affective (e.g., motivation and attitudes) and cognitive (e.g.,
494 PERSONNEL PSYCHOLOGY
memory, knowledge base, and executive control) structures interact with
each other and with sensory information from the simulation game in
order to enhance trainees’ knowledge base. The process is iterative as
sensory information continuously interacts with trainees’ cognitive sys-
tem and new information is stored. According to the theory, both affective
and cognitive structures are essential components of the cognitive system,
and simulation games should be more effective than other instructional
methods because they target both of these structures.
Together these theories suggest that, ideally, simulation games utilize
a combination of entertainment and active learning principles to immerse
trainees in learning the course material. Entertainment will ensure trainees
repeatedly engage in the learning experience, enhancing learner motiva-
tion. Active learning principles provide trainees with responsibility for
making important learning decisions and rely on inductive learning in
which trainees must explore the task in order to infer the rules for effec-
tive performance (Bell & Kozlowski, 2008; Frese, Brodbeck, Heinbokel,
Mooser, Schleiffenbaum, & Thiemann, 1991). Learning and adaptive
transfer are enhanced via self-regulatory processes when active learn-
ing principles are incorporated in the training design (Bell & Kozlowski,
2008). However, simulation games do not always utilize entertainment
and active learning principles to enhance the learning experience.
In the following sections, I rely on these theoretical frameworks for
hypothesizing the effects of simulation games on affective and cognitive
training outcomes. I then rely on theory and frameworks of simulation
game characteristics (e.g., Garris et al., 2002; Tennyson & Jorczak, 2008;
Wilson et al., 2009) to develop hypotheses regarding the characteristics
of simulation games and the instructional context that enhance learning.
Effect of Simulation Games on Training Outcomes
My goal was to examine the effect of simulation games on a compre-
hensive set of training outcomes. Kraiger, Ford, and Salas (1993) proposed
that learning is multidimensional and may be observed by changes in affec-
tive, cognitive, or skill capabilities. Furthermore, simulation game theories
emphasize that affective, behavioral, and cognitive processes are all criti-
cal indicators of training effectiveness (Garris et al., 2002; Malone, 1981;
Tennyson & Jorczak, 2008). Thus, I reviewed the literature in order to
determine the effect of simulation games, relative to a comparison group,
on three affective (i.e., motivation, trainee reactions, and self-efﬁcacy),
one behavioral (i.e., effort), two cognitive (i.e., declarative knowledge
and retention), and two skill-based (i.e., procedural knowledge and trans-
fer) training outcomes. Although other evaluation criteria are valuable
for clarifying the effectiveness of simulation games, insufﬁcient primary
TRACI SITZMANN 495
studies were available to examine other indicators of training effectiveness.
Below I review the literature on the effect of simulation games on training
outcomes and present hypotheses for the four outcomes—self-efﬁcacy,
declarative knowledge, procedural knowledge, and retention—where suf-
ﬁcient research has been conducted to calculate the meta-analytic effect
Motivation, effort, and trainee reactions. Malone (1981) and Garris
et al. (2002) both suggested that simulation games are effective because
they target affective processes. There is a cyclical relationship among
trainees’ enjoyment of game play, intrinsic motivation, and the decision to
continue playing (Garris et al., 2002). Thus, both of these theories suggest
that motivation, effort, and trainee reactions are key training outcomes that
should be affected by simulation games. Motivation is a psychological
training outcome and refers to the extent to which trainees strive to learn
the content of a training program, whereas effort is a behavioral outcome
and refers to the amount of time and energy devoted to training (Fisher
& Ford, 1998; Noe, 1986; Sitzmann & Ely, 2010). Reactions refer to
trainees’ satisfaction with the instructional experience (Sitzmann, Brown,
Casper, Ely, & Zimmerman, 2008).
Despite these theoretical assertions, this review revealed that a limited
range of studies has compared a simulation game group to a comparison
group on these three outcomes—only one study has compared posttraining
motivation (DeRouin-Jessen, 2008), two studies have compared effort
levels (DeRouin-Jessen, 2008; Sukhai, 2005), and three have compared
trainee reactions (DeRouin-Jessen, 2008; Parchman, Ellis, Christinaz, &
Vogel, 2000; Ricci et al., 1996). Thus, the scarcity of research in this
area precludes an empirical test of the effect of simulation games on
posttraining motivation, effort, and trainee reactions.
Self-efﬁcacy. Posttraining self-efﬁcacy refers to trainees’ conﬁdence
that they have learned the information taught in training and can per-
form training-related tasks (Bandura, 1997). In contrast to the aforemen-
tioned training outcomes, sufﬁcient empirical research has been conducted
to compare posttraining self-efﬁcacy for trainees taught with simulation
games, relative to a comparison group. A critical precursor to high self-
efﬁcacy is experience with work-related tasks (Bandura, 1991). Simulation
games are interactive and tend to be more engaging than other instruc-
tional methods (Ricci et al., 1996; Vogel et al., 2006). High interactivity
and the opportunity to make choices while participating in simulation
games may result in trainees feeling empowered, ultimately enhancing
trainees’ self-efﬁcacy (Bandura, 1993; Tennyson & Jorczak, 2008). Sim-
ulation games should also promote mastery of the material via letting
the trainee attempt to apply the knowledge and skills, enhance metacog-
nitive activity due to actively engaging with the material, and promote
496 PERSONNEL PSYCHOLOGY
positive emotional arousal, all of which have positive effects on self-
efﬁcacy (Bandura, 1977; Bell & Kozlowski, 2008; Brown & Ford, 2002;
Garris et al., 2002; Kozlowski & Bell, 2006; Malone, 1981). This is con-
sistent with empirical research by Randell, Hall, Bizo, and Remington
(2007) that found simulation games resulted in higher posttraining self-
efﬁcacy than a comparison group when learning how to treat children with
Hypothesis 1: Posttraining self-efﬁcacy will be higher for trainees in
the simulation game group than the comparison group.
Learning. I also compared simulation games to a comparison group
in terms of their effect on four learning outcomes: declarative knowl-
edge, procedural knowledge, retention, and training transfer. Declarative
knowledge refers to trainees’ memory of the facts and principles taught in
training and the relationship among knowledge elements (Kraiger et al.,
1993). Procedural knowledge refers to information about how to perform
a task or action. Retention is a delayed assessment of declarative knowl-
edge and refers to trainees’ memory of the factual information taught in
training several weeks or months after leaving the training environment.
Finally, training transfer refers to the successful application of the skills
gained in a training context to the job (Baldwin & Ford, 1988).
Interactive cognitive complexity theory proposes that simulation
games maximize learning because they simultaneously engage the af-
fective and cognitive processes of trainees (Tennyson & Breuer, 1997;
Tennyson & Jorczak, 2008). Simulation games tend to be more interactive
than other instructional methods, and interactivity is a critical compo-
nent of effective instruction (Jonassen, 2002; Northup, 2002; Sitzmann,
Kraiger, Stewart, & Wisher, 2006). Several previous reviews have ex-
amined the effect of simulation games on cognitive learning outcomes.
Randel et al. (1992) reviewed the effectiveness of simulation games, pri-
marily for teaching children, and found 27 out of 68 studies favored the
use of simulation games over classroom instruction, whereas 3 favored
classroom instruction. Moreover, 14 studies examined retention, and 10
of these studies found retention was greater for trainees taught with sim-
ulation games than classroom instruction. Similarly, Vogel et al. (2006)
conducted a meta-analysis of the instructional effectiveness of simulation
games for teaching children and adult learners. When averaging across
these trainee populations and learning dimensions, they found learning
gains were greater for trainees taught with simulation games than tradi-
tional teaching methods (z=6.05, N=8,549).
Only one study has compared simulation games to other instruc-
tional methods for enhancing training transfer. Meyers, Strang, and Hall
(1989) found trainees who used a simulation game to practice counseling
TRACI SITZMANN 497
preschoolers signiﬁcantly outperformed trainees who learned by coding
audiotapes of children’s disﬂuency on six out of eight transfer measures
(dranged from −.21 to 2.39; N=20). Despite the paucity of research
examining transfer, there are a sufﬁcient number of studies to conduct a
meta-analysis for the other three learning outcomes (declarative knowl-
edge, procedural knowledge, and retention), and based on interactive cog-
nitive complexity theory and previous research, I hypothesize a learning
advantage for trainees taught with simulation games.
Hypotheses 2–4: Posttraining declarative knowledge (Hypothesis 2),
posttraining procedural knowledge (Hypothesis 3),
and retention of the training material (Hypothesis 4)
will be higher for trainees in the simulation game
group than the comparison group.
Moderators of the Effectiveness of Simulation Games
A second objective of the study is to examine moderators of the ef-
fectiveness of simulation games relative to the comparison group. The
moderator variables were chosen based on the features of simulation
game design and the instructional situation discussed in previous reviews
of this literature (e.g., Bell et al., 2008; Garris, et al. 2002; Hays, 2005;
Malone, 1981; Wilson et al., 2009) as well as course design features that
theory and previous meta-analyses indicate inﬂuence learning (e.g., Bell
& Kozlowski, 2008; Brown & Ford, 2002; Keith & Frese, 2008; Sitzmann
et al., 2006). In the following sections, I hypothesize the moderating ef-
fect of two simulation game characteristics (i.e., entertainment value and
whether the majority of instruction in the simulation game was active or
passive), two instructional context characteristics (i.e., whether trainees
had unlimited access to the simulation game and whether the simulation
game was the sole instructional method for the treatment group), and
one characteristic of the comparison group (i.e., whether the instructional
methods used to teach the comparison group as a substitute for the simu-
lation game were active or passive) on learning from a simulation game
relative to the comparison group. Finally, four methodological moderators
were examined in order to ensure that observed differences in effect sizes
are driven by the hypothesized moderators rather than other factors.
Entertainment value. Simulation games that were high in entertain-
ment value contained at least one feature common to either board games
or video games including rolling virtual dice and moving pegs around a
board, striving to make the list of top scorers, playing the role of a character
in a fantasy world, and shooting foreign objects. For example, Moshir-
nia (2008) had players assume the role of either George Washington or
498 PERSONNEL PSYCHOLOGY
King George III as they fought other characters while learning about the
American Revolutionary War. This simulation game had several entertain-
ing features including playing the role of a character in a fantasy world
and ﬁghting other characters. The simulation game utilized by Boyd and
Murphrey (2002) was much less entertaining. Players assumed the role of
a human resource director with personal knowledge of the background of
a job applicant. The objective of the simulation game was to improve lead-
ership skills as players decided whether to reveal what they knew about
the candidate to the search committee and discovered the consequences
of their actions.
Malone (1981) theorized that the features that make a simulation game
intrinsically motivating are challenge, curiosity, and fantasy. Intrinsically
motivating features of simulation games increase self-determination be-
cause trainees choose to engage in game play as they ﬁnd it interesting and
enjoyable (Deci & Ryan, 1985). Moreover, researchers have demonstrated
that instruction embedded in a fantasy context increases both interest and
learning (Cordova & Lepper, 1996; Parker & Lepper, 1992). Trainees be-
come immersed in entertaining and fantasy-based simulation games more
than other instructional methods, thereby increasing learning (Cordova &
Lepper, 1996; Garris et al. 2002; Wilson et al., 2009).
Hypothesis 5: The entertainment value of the simulation game will
moderate learning from simulation games; relative to
the comparison group, trainees will learn more from
simulation games that are high rather than low in en-
Activity level of the simulation game group. Consistent with the deﬁ-
nition advanced for simulation games, all are interactive. However, some
utilize interaction to keep trainees engaged (e.g., ﬁghting other charac-
ters), but the interaction does not contribute to learning the course material.
Indeed, some of the simulation games included in the review presented
the majority of the learning content in a passive manner via text or audio
explanations. For example, Parchman et al. (2000) embedded presenta-
tions in the simulation game so trainees could review course topics or
participate in linear computer-based instruction.
Some theorists propose that learning requires active engagement with
the material (Brown & Ford, 2002; Jonassen, 2002). Active learning en-
hances metacognition; that is, trainees who actively learn the material
exert more cognitive effort to evaluate information and integrate it with
their existing knowledge base (Bell & Kozlowski, 2008; Brown & Ford,
2002). Practicing the key components of a task during training should
help trainees develop an understanding of the deeper, structural features
of the task (Newell, Rosenbloom, & Laird, 1989; Sitzmann, Ely, & Wisher,
TRACI SITZMANN 499
2008). Teaching core training material in a passive manner in simulation
games is contrary to theory suggesting that one of the instructional ad-
vantages of simulation games is that they engage trainees in the learning
experience (Chen & O’Neil, 2008; Garris et al., 2002). By relying on pas-
sive instruction in a simulation game, it dilutes the instructional advantage
of the simulation game.
Hypothesis 6: The activity level of the instruction in the simula-
tion game will moderate learning from simulation
games; relative to the comparison group, trainees will
learn more from simulation games that actively engage
trainees in learning rather than passively conveying the
Unlimited access to the simulation game. Garris et al.’s (2002) model
theorizes that one of the advantages of simulation games is that they are
intrinsically motivating. There is a cyclical relationship among users’ en-
joyment of the simulation game, the decision to continue playing, and
feedback on one’s performance. Learning beneﬁts occur when trainees
choose to repeatedly engage in game play, mastering the skills that are
taught. Thus, the full learning potential of simulation games is only real-
ized if trainees can access the simulation game as many times as desired.
Consistent with this argument, cognitive learning theorists (Bruner, 1962;
Piaget, 1951) argued that intrinsically motivating play-type activities are
crucial for deep learning (Malone, 1981). When trainees participate in
traditional learning activities, they rarely display the level of effort and
motivation that is typical of simulation games, thereby limiting the learn-
ing potential (Tennyson & Jorczak, 2008).
Hypothesis 7: Whether trainees have unlimited access to the simu-
lation game will moderate learning from simulation
games; relative to the comparison group, trainees will
learn more from simulation games when they have un-
limited access to the simulation game than when access
to the simulation game is limited.
Simulation game as sole instructional method. Courses differed in
terms of whether the simulation game was the only instruction the treat-
ment group received (e.g., Kim, Kim, Min, Yang, & Nam, 2002; North
et al., 2003) or the simulation game was used as a supplement to other
instructional methods (e.g., Ebner & Holzinger, 2007; Ortiz, 1994). Garris
et al.’s (2002) model proposes that a debrieﬁng session, to review and an-
alyze what happened during game play, mediates the relationship between
the game cycle and learning. Lee’s (1999) review of the literature revealed
that the instructional effectiveness of simulations, relative to a comparison
500 PERSONNEL PSYCHOLOGY
group, is enhanced when trainees have the opportunity to review informa-
tion before practicing in the simulation. Moreover, Hays (2005) proposed
that simulation games should be embedded in instructional programs that
elaborate on how the information conveyed in the game is pertinent to
trainees’ jobs. Rarely are simulation games so well designed that trainees
can learn an instructional topic through game play alone (Tennyson &
Jorczak, 2008). When training utilizes a breadth of instructional methods,
trainees who are having difﬁculty learning the material can continue to
review the material with multiple instructional methods to increase their
mastery of the course content (Sitzmann et al., 2006; Sitzmann, Ely, et al.,
Hypothesis 8: Whether simulation games are embedded in a program
of instruction will moderate learning from simulation
games; relative to the comparison group, trainees will
learn more from simulation games that are embedded
in a program of instruction than when they are the sole
Activity level of the comparison group. Trainees in the comparison
group were often taught via a different instructional method as a substitute
for utilizing the simulation game. However, studies differed in terms of
whether the comparison group learned by means of active (e.g., Hughes,
2001; Mitchell, 2004; Willis, 1989) or passive (e.g., Bayrak, 2008; Frear &
Hirschbuhl, 1999; Shute & Glaser, 1990) instructional methods. Trainees
are active when they are reviewing with a computerized tutorial, partici-
pating in a discussion, and completing assignments. Trainees are passive
when they are listening to a lecture, reading a textbook, or watching a
One of the advantages of simulation games is that they typically require
trainees to be active while learning the course material (Ricci et al., 1996;
Vogel et al., 2006). Actively engaging with the course material enhances
learning (Newell et al., 1989; Sitzmann et al., 2006; Webster & Hackley,
1997), regardless of whether trainees are participating in a simulation
game or learning from another instructional method. Active learning as-
sists trainees in developing both a reﬁned mental model of the training
topic and the adaptive expertise necessary to apply trained skills under
changing circumstances (Bell & Kozlowski, 2008; Keith & Frese, 2005,
2008). Thus, the difference in learning between the simulation game and
comparison groups should be less when the comparison group is active
while learning the course material.
Hypothesis 9: The activity level of the comparison group will moder-
ate learning; relative to trainees taught with simulation
TRACI SITZMANN 501
games, the comparison group will learn more when they
are taught with active rather than passive instructional
Methodological moderators. One of the advantages of meta-analysis
is it allows for a comparison of studies that differ in experimental rigor
and other methodological factors (Lipsey, 2003). It is only by controlling
for methodological artifacts that one can be certain that observed differ-
ences in effect sizes are driven by the hypothesized moderators rather than
factors that are spuriously correlated with the outcome variable. Thus, this
meta-analysis examined whether the effect of simulation games, relative
to a comparison group, on learning was related to four methodologi-
cal moderators: random assignment to experimental conditions, rigor of
the study design (pretest–posttest vs. posttest only), publication status
(published vs. unpublished), and year of the publication, dissertation, or
presentation. Media comparison studies often confound instructional me-
dia with instructional quality, student motivation, and other factors (Clark,
1983, 1994; Sitzmann et al., 2006). Randomly assigning trainees to ex-
perimental conditions and utilizing a rigorous study design would allow
researchers to rule out alternative explanations for differences in learning
between simulation game and comparison groups. The publication status
moderator analysis will reveal whether there is evidence of a ﬁle drawer
problem in simulation games research (Begg, 1994). That is, do published
studies tend to report effect sizes that are larger in magnitude than un-
published studies? Finally, the year moderator results will clarify whether
the effectiveness of simulation games, relative to a comparison group, has
increased with improvements in training technology in recent years.
Computer-based literature searches of PsycInfo, ERIC, and Digital
Dissertations were used to locate relevant studies. To be included in the
initial review, each abstract had to contain terms relevant to games or
simulations and training or education. Initial searches resulted in 4,545
possible studies. A review of abstracts limited the list to 264 potentially
relevant reports, of which 40 met the inclusion criteria. In addition, I
manually searched reference lists from recently published reports focusing
on the effectiveness of simulation games (e.g., Lee, 1999; Schenker, 2007;
Tobias & Fletcher, 2007; Vogel et al., 2006; Wilson et al., 2009). These
searches identiﬁed an additional 13 reports.
502 PERSONNEL PSYCHOLOGY
A search for additional published and unpublished studies was also
conducted. First, I manually searched the Academy of Management and
Society for Industrial and Organizational Psychology conference pro-
grams. Second, practitioners and researchers with expertise in training
were asked to provide leads on published and unpublished work. In all,
I contacted 117 individuals. These efforts identiﬁed an additional two
studies for a total of 55 reports, yielding 65 independent samples.
Due to the upward bias in effect sizes from gain score research (Lipsey
& Wilson, 2001), this report focuses exclusively on studies that com-
pared posttraining outcomes for simulation game and comparison groups.
Trainees in the simulation game group received all or some of their learn-
ing content via a simulation game. The comparison group differed across
studies and ranged from a no-training control condition to trainees who re-
ceived alternative instructional methods as a substitute for the simulation
To be included in this review, studies had to meet four additional cri-
teria: (a) The article reported results that allowed for the calculation of
adstatistic—group means and standard deviations, a correlation, t-test,
or univariate F-test; (b) data had to be collected at the individual-level of
analysis; data collected at the group-level were excluded (e.g., Brannick,
Prince, & Salas, 2005; Rapp & Mathieu, 2007) because there was in-
sufﬁcient research to conduct a meta-analysis for team-based training
outcomes; (c) participants were nondisabled adults ages 18 or older; (d)
the training facilitated potentially job-relevant knowledge or skills (i.e.,
not coping with physical or mental health challenges). The last two criteria
support generalization to work-related adult training programs.
Coding and Interrater Agreement
In addition to recording all relevant effect sizes and sample sizes, the
following information was coded from each study: (a) self-efﬁcacy, (b)
learning outcomes, (c) entertainment value, (d) majority of simulation
game instruction was active or passive, (e) whether trainees could access
the simulation game as many times as desired, (f) simulation game as
standalone instruction or supplement to other instructional methods, (g)
activity level of the instruction the comparison group received as a sub-
stitute for the simulation game, (h) random assignment to experimental
conditions, (i) rigor of the study design, (j) publication status, and (k) year
of the publication, dissertation, or presentation. Scales for each moderator
TRACI SITZMANN 503
were drafted prior to coding and modiﬁed following initial attempts to
code articles. The coding rules are described below.
Self-efﬁcacy. Posttraining self-efﬁcacy refers to trainees’ conﬁdence
that they have learned the information taught in training and can perform
training-related tasks (Bandura, 1997). For example, Ricci et al. (1996)
measured self-efﬁcacy by asking trainees to rate their conﬁdence that they
would remember what they had learned that day.
Learning outcomes. Declarative and procedural knowledge were
coded based on Kraiger et al.’s (1993) multidimensional framework of
learning. Declarative outcomes are knowledge assessments designed to
measure if trainees remembered concepts presented during training; they
were always assessed with a written test. Procedural outcomes were de-
ﬁned as the ability to perform the skills taught in training. They were
assessed by participating in an activity (e.g., simulation or role-play) or
with a written test that required trainees to demonstrate memory of the
steps required to complete the skills taught in training. Retention was
coded as delayed measures of declarative knowledge. The majority of
studies assessed retention between 1 and 4 weeks after the end of train-
ing, but one study—Lawson, Shepherd, and Gardner (1991)—assessed
retention between 88 and 167 days after training ended.
Entertainment value. Simulation games were coded as having either a
high or low level of entertainment value. Simulation games had a high level
of entertainment value when they contained at least one feature common
to either board games or video games including rolling virtual dice and
moving pegs around a board, striving to make the list of top scorers,
playing the role of a character in a fantasy world, and shooting foreign
objects. For example, DeRouin-Jessen (2008) examined the effectiveness
of an entertaining simulation game in which trainees maneuvered through
a virtual town while learning about equal employment opportunity laws.
The simulation game included elements of fantasy, such as a “mentor
character” who appeared as a ﬂoating ball of light and spoke to participants
in an “ethereal voice” (p. 67). The McGill negotiation simulator was low
in entertainment value (Ross, Pollman, Perry, Welty, & Jones, 2001);
trainees spent up to an hour negotiating the sale of an airplane.
Simulation game instruction was passive or active. Studies were
coded as to whether the majority of the instructional content in the sim-
ulation game was passively or actively conveyed to trainees. Simulation
games that taught the majority of the course content via text or audio
explanations were coded as providing trainees with passive instruction;
simulation games that taught the majority of the course content via ac-
tivities, practice, and engaging with the simulation game were coded as
providing trainees with active instruction. For example, DeRouin-Jessen
(2008) examined a simulation game that taught with passive instruction.
504 PERSONNEL PSYCHOLOGY
Trainees navigated a virtual environment (during which time they were not
learning course content) and then stopped to read factual information on
equal employment opportunity laws from books in the simulation game.
In contrast, Gopher, Weil, and Bareket’s (1994) simulation game relied on
active instruction as trainees honed ﬂight-relevant skills by controlling a
spaceship while defending it from mines and trying to destroy an enemy’s
Unlimited access to the simulation game. Studies were coded as to
whether trainees were allowed to utilize the simulation game as many
times as desired (e.g., Cataloglu, 2006; Sterling & Gray, 1991) or a
limit was imposed on trainees’ interaction with the simulation game (e.g.,
Hughes, 2001; Mitchell, 2004).
Simulation game as standalone instruction. Studies were coded as
to whether the simulation game was the sole instructional method used
to teach the treatment group (e.g., Desrochers, Clemmons, Grady, &
Justice, 2000; Faryniarz & Lockwood, 1992) or the simulation game was
a supplement to other instructional methods (e.g., Sukhai, 2005; Willis,
Activity level of comparison group. I also examined whether the
comparison group received instruction as a substitute for the instruc-
tion received in the simulation game and, if so, whether the instructional
methods used to teach the comparison group were active or passive. Pas-
sive instructional methods include listening to lectures and reading case
studies or textbooks (e.g., Ivers & Barron, 1994; Taylor & Chi, 2006).
Active instructional methods include computerized tutorials, completing
assignments, and conducting laboratory experiments (e.g., Hughes, 2001;
Zacharia, 2007). However, several studies did not provide instruction for
the comparison group as a substitute for utilizing the simulation game
(e.g., Cameron & Dwyer, 2005; Ortiz, 1994).
Methodological moderators. Four methodological moderators were
coded: random assignment to experimental conditions, rigor of the study
design (pretest–posttest vs. posttest only), publication status (published vs.
unpublished), and year of the publication, dissertation, or presentation.
Coding and Interrater Agreement
All articles were coded independently by two trained raters. Interrater
agreement (Cohen’s kappa) was excellent according to Fleiss (1981) for
each of the coded variables, with a coefﬁcients ranging from .82 to .98 for
each of the moderator variables. All coding discrepancies were discussed
until a consensus was reached.
TRACI SITZMANN 505
Calculating Effect Size Statistic (d) and Analyses
The Hedges and Olkin (1985) approach was used to analyze the data.
The effect size calculated for each study was d, the difference between the
simulation game and the comparison group, divided by the pooled standard
deviation. When means and standard deviations were not available, effect
sizes were calculated from a correlation, t-test, or univariate F-test using
formulas reported in Glass, McGaw, and Smith (1981) and Hunter and
Schmidt (1990). Effect sizes were corrected for small-sample bias using
formulas provided by Hedges and Olkin (1985). The self-efﬁcacy effect
sizes were corrected for attenuation using the scale reliabilities reported
in each study. When a study failed to provide a coefﬁcient alpha reliability
estimate, I used the average self-efﬁcacy reliability across all samples
from this study and from Sitzmann, Brown, et al. (2008). The average
reliability was .83. I did not correct the learning outcomes effect sizes
for attenuation due to the lack of available test–retest or alternate forms
Occasionally a single study would report data from two simulation
game training groups and/or two comparison groups. In these situations,
an effect size was calculated for all possible simulation game–comparison
group pairs and averaged by weighting each of the effect sizes by the sum
of the sample size of the independent group and one half of the sample
size of the nonindependent group. Thus, the nonindependent sample was
weighted according to its sample size in the overall effect size. In addition,
whenever a single study reported multiple effect sizes based on the same
sample for a single criterion, the effect size that was most similar to the
other assessments of that particular relationship was used in the meta-
analysis. Studies that included multiple independent samples were coded
separately and treated as independent.
Finally, 95% conﬁdence intervals were calculated around the weighted
mean ds. Conﬁdence intervals assess the accuracy of the estimate of the
mean effect size and provide an estimate of the extent to which sampling
error remains in the weighted mean effect size (Whitener, 1990).
Prior to ﬁnalizing the analyses, a search for outliers was conducted us-
ing Huffcutt and Arthur’s (1995) sample-adjusted meta-analytic deviancy
(SAMD) statistic. Based on the results of these analyses, Blunt (2007) was
identiﬁed as a potential outlier with a SAMD value of 7.79. A review of the
descriptive statistics reported in Blunt revealed that the learning outcome
506 PERSONNEL PSYCHOLOGY
(grade in course) included zeros for multiple participants in the compari-
son group. Thus, Blunt included participants in the learning analysis even
if they dropped out of training. As such, the three samples reported in
Blunt were removed from all analyses.
Hedges and Olkin’s (1985) homogeneity analysis was used to deter-
mine whether the effect sizes were consistent across studies. For main
effect analyses, the set of effect sizes was tested for homogeneity with the
QTstatistic. QThas an approximate χ2distribution with k– 1 degrees of
freedom, where kis the number of effect sizes. If QTexceeds the critical
value, then the null hypothesis of homogeneity is rejected. Rejection indi-
cates that there is more variability in effect sizes than expected by chance,
suggesting that it is appropriate to test for moderators.
The goal of the moderator analyses was to examine if the effectiveness
of simulation games, relative to the comparison group, differed based on
features of simulation games and the comparison group, characteristics of
the instructional context, and methodological factors. Moderating effects
were tested by classifying studies according to the moderator categories
and testing for homogeneity between and within categories (Lipsey &
Wilson, 2001). For each moderator, a between-class goodness-of-ﬁt statis-
tic, QB, was calculated to test for homogeneity of effect sizes across mod-
erator categories. It has an approximate χ2distribution with j–1degrees
of freedom, where jis the number of moderator categories. If QBexceeds
the critical value, it indicates that there is a signiﬁcant difference across
moderator categories; this is analogous to a signiﬁcant main effect in an
analysis of variance. In addition, a within-class goodness-of-ﬁt statistic,
QW, was calculated to test for homogeneity of effect sizes within each
moderator category. It has an approximate χ2distribution with k–jde-
grees of freedom, where kis the number of effect sizes included in the
analysis. If QWexceeds the critical value, it indicates that the effect sizes
within the moderator categories are heterogeneous.
The moderating effect of year of publication, dissertation, or pre-
sentation was tested with a correlation weighted by the inverse of the
sampling-error variance between the moderator variable and the effect
sizes. A limitation of the subgroup approach to moderator analyses is the
inability to account for the joint effect of correlated moderators. Thus,
I also utilized weighted least squares (WLS) regression to examine the
joint effect of the moderators on learning. Effects sizes were weighted
by the inverse of the sampling error variance as described by Steel and
TRACI SITZMANN 507
Meta-Analytic Results for Self-Efﬁcacy and Cognitive Learning Outcomes
Comparing Trainees Taught With Simulation Games to a Comparison Group
derror kNLower Upper QT
Self-efﬁcacy 0.52 .10 8 506 0.32 0.72 38.33∗
Declarative knowledge 0.28 .04 39 2,758 0.20 0.36 283.99∗
Procedural knowledge 0.37 .07 22 936 0.23 0.50 85.66∗
Retention 0.22 .08 8 824 0.07 0.37 67.03∗
Note.d=inverse variance weighted mean effect size; k=number of effect sizes included
in the analysis; N=sum of the sample sizes for each effect size included in the analysis;
∗indicates the QTvalue is statistically signiﬁcant at the .05 level and the effect sizes are
Fifty-ﬁve research reports contributed data to the meta-analysis, in-
cluding 39 published reports, 12 dissertations, and 4 unpublished reports.
These reports included data from 65 samples and 6,476 trainees. Learners
were undergraduate students in 77% of samples, graduate students in 12%
of samples, employees in 5% of samples, and military personnel in 6% of
samples.1Across all samples providing demographic data, the average age
of trainees was 23 years and 52% were male. A majority of the researchers
who contributed data to the meta-analysis were in the ﬁelds of education
(25%) and psychology (25%), whereas 12% were in business; 11% were
in educational technology; 9% were in medicine; 6% were in computer
science, math, or engineering; 5% were in science; and 7% were in other
Main Effect Analyses
The main effects are presented in Table 1. The ﬁrst hypothesis pre-
dicted that trainees receiving instruction via a simulation game would have
higher levels of posttraining self-efﬁcacy than trainees in the comparison
group. Across eight studies, self-efﬁcacy was 20% higher for trainees
1The effect of simulation games, relative to a comparison group, on learning did not
signiﬁcantly differ across undergraduate, graduate, employee, or military populations, QB=
χ2(3) =3.63, p>.05.
508 PERSONNEL PSYCHOLOGY
receiving instruction via a simulation game than trainees in a compari-
son group (d=.52). In addition, the conﬁdence interval for self-efﬁcacy
excluded zero, supporting Hypothesis 1.
Hypotheses 2 through 4 predicted that trainees receiving instruction
via a simulation game would learn more than trainees in a comparison
group. Trainees receiving instruction via a simulation game had higher
levels of declarative knowledge (d=.28),2procedural knowledge (d=
.37), and retention (d=.22) than trainees in the comparison group. On
average, trainees in the simulation game group had 11% higher declara-
tive knowledge levels, 14% higher procedural knowledge levels, and 9%
higher retention levels than trainees in the comparison group. Moreover,
the conﬁdence intervals for all three learning outcomes excluded zero,
providing support for Hypotheses 2 though 4.
Based on the similarity of the learning effect sizes and the overlapping
conﬁdence intervals, I tested the homogeneity of effect sizes for the three
cognitive learning outcomes. The QBwas not signiﬁcant (χ2(2) =2.21,
p>.05), suggesting that the mean effect sizes for declarative knowledge,
procedural knowledge, and retention did not differ by more than sam-
pling error. As such, the three learning outcomes were combined for the
Table 2 presents the mean effect sizes and estimates of homogeneity
between (QB) and within (QW) the moderator subgroups. A signiﬁcant
QBindicates that the mean effect sizes across categories of the moderator
variable differ by more than sampling error, suggesting that the moderator
variable is having an effect (Lipsey & Wilson, 2001). The QBstatistic was
signiﬁcant for all of the hypothesized moderators except entertainment
Hypothesis 5 predicted that, relative to the comparison group, trainees
will learn more from simulation games that are high rather than low in
entertainment value. Relative to the comparison group, trainees learned
the same amount from simulation games that had high (d=.26) or low
(d=.38) entertainment value, failing to support Hypothesis 5.
Hypothesis 6 proposed that trainees will learn more from simula-
tion games that actively engage them in learning rather than passively
conveying the instructional material, relative to the comparison group.
When the majority of the instruction in the simulation game was pas-
sive, the comparison group learned more than the simulation game group
2One outlier was identiﬁed in this study—Blunt (2007)—who reported data from three
samples. When Blunt was included in the declarative knowledge analysis d=.55 (k=42,
TRACI SITZMANN 509
Meta-Analytic Moderator Results Comparing Learning From Simulation Games
to a Comparison Group
of effect sizes
derror kNLower Upper QBQW
High .26 .08 9 809 .11 .41 1.84 350.57∗
Low .38 .04 51 3,216 .31 .45
Simulation game instruction was active or passive
Active .49 .04 46 3,260 .41 .56 36.41∗251.56∗
Passive −.11 .09 9 521 −.29 .07
Unlimited access to the simulation game
Yes .68 .07 10 925 .54 .82 21.11∗265.06∗
No .31 .04 45 2,738 .23 .38
Simulation game as sole instructional method
Game is a supplement .51 .04 40 3,109 .43 .58 64.59∗304.19∗
Game is standalone −.12 .07 21 946 −.26 .01
Activity level of instruction the comparison group received as a substitute for simulation game
Active −.19 .07 14 832 −.33 −.05 84.48∗277.35∗
Hands on practice −.13 .12 4 282 −.37 .11
Computerized tutorial −.70 .13 4 273 −.95 −.45
Assignment .86 .29 3 55 .30 1.42
Discussion .13 .18 1 130 −.21 .48
Group activities &
−.11 .28 1 50 −.67 .44
.12 .31 1 42 −.49 .72
Passive .38 .07 18 970 .24 .51
Lecture .45 .10 8 457 .25 .65
Reading .42 .10 8 419 .22 .62
Watching a video .50 .41 1 24 −.31 1.32
Video & reading −.30 .24 1 70 −.77 .17
Combination of lecture
& active instructional
.38 .10 6 425 .18 .58
No instruction .61 .05 25 1,844 .51 .71
Random assignment to experimental conditions
Yes .35 .05 34 1,997 .26 .45 1.37 301.89∗
No .43 .05 25 1,931 .34 .53
Rigor of the study design
Pretest–posttest .36 .05 28 1,832 .26 .46 .00 352.41∗
Posttest only .36 .05 32 2,193 .27 .45
Published .52 .04 44 3,032 .44 .59 64.91∗287.50∗
Unpublished −.10 .07 16 993 −.23 .03
Note.d=inverse variance weighted mean effect size; k=number of effect sizes included in
the analysis; N=sum of the sample sizes for each effect size included in the analysis; QB=
between-class goodness-of-ﬁt statistic; QW=within-class goodness-of-ﬁt statistic.
∗indicates the Qvalue is statistically signiﬁcant at the .05 level.
510 PERSONNEL PSYCHOLOGY
(d=−.11). However, when the majority of the instruction in the simula-
tion game was active, the simulation game group learned more than the
comparison group (d=.49). These ﬁndings provide support for Hypoth-
esis 6 and suggest that simulation games are more effective when they
actively engage trainees in learning the course material.
Hypothesis 7 predicted that, relative to the comparison group, trainees
will learn more from simulation games when they can utilize the sim-
ulation game as many times as desired than when trainees have lim-
ited access to the simulation game. In support of Hypothesis 7, trainees
in the simulation game group outperformed the comparison group to a
greater extent when they had unlimited access to the simulation game
(d=.68) than when trainees had limited access to the simulation game
Hypothesis 8 predicted that trainees will learn more from simulation
games that are embedded in a program of instruction than when simulation
games are the sole instructional method, relative to the comparison group.
Consistent with Hypothesis 8, when simulation games were used as a
supplement to other instructional methods, the simulation game group had
higher knowledge levels than the comparison group (d=.51). However,
when simulation games were used as standalone instruction, trainees in
the comparison group learned more than trainees in the simulation game
Hypothesis 9 predicted that, relative to trainees taught with simulation
games, the comparison group will learn more when they are taught with
active rather than passive instructional methods. In support of Hypothe-
sis 9, the comparison group learned more than the simulation game group
when they were taught with active instructional methods (d=−.19). How-
ever, the simulation game group learned more than the comparison group
when the comparison group was taught with passive instructional methods
(d=.38) or a combination of lecture and active instructional methods (d=
.38). In addition, the effect size was largest when the comparison group
did not receive instruction as a substitute for the instruction received in the
simulation game (d=.61). Follow-up analyses examined which instruc-
tional methods have been compared to simulation games and whether the
speciﬁc instructional methods used to teach the comparison group inﬂu-
enced the effect size. Hands-on practice of the information taught in the
simulation game, computerized tutorials, and assignments were the active
instructional methods that have been compared to simulation games with
the greatest frequency, and the effect sizes varied greatly across these three
instructional methods. Computerized tutorials were much more effective
than simulation games (d=−.70), and hands-on practice was slightly
more effective than simulation games (d=−.13). In contrast, simulation
games were much more effective than assignments (d=.86). With regards
TRACI SITZMANN 511
to passive instructional methods, simulation games were more effective
than lecture (d=.45) and reading (d=.42), which were the two most
common instructional methods used to teach the comparison group.
Next, I calculated effect sizes for studies where the simulation game
and comparison groups were matched in terms of the activity level of the
instruction. When both instructional methods utilized active instruction,
simulation games were 1% less effective than comparison instructional
methods (d=−.02, k=10, N=627); when both instructional methods
utilized passive instruction, simulation games were 3% less effective than
comparison instructional methods (d=−.07, k=6, N=383). Thus, the
effectiveness of simulation games and alternative instructional methods is
similar when the instructional methods are matched in terms of the extent
to which they actively engage trainees in learning the material.
Turning now to methodological variables, random assignment to ex-
perimental conditions (d=.35 and .43 for studies with and without random
assignment, respectively) and the rigor of the study design (d=.36 for
both pretest–posttest and posttest only designs) did not moderate learning
from simulation games, relative to the comparison group (QB=1.37 and
.00, respectively, p>.05). However, effect sizes were much larger for
published (d=.52) than unpublished (d=−.10) studies (QB=64.91,
p<.05). Finally, the inverse of the sampling error variance weighted cor-
relation between the year of the publication, dissertation, or presentation
and the effect size was not statistically signiﬁcant (r=.16). This suggests
that the effect of simulation games, relative to the comparison group, on
learning has not changed over time.
Overall the results indicated that four of the ﬁve hypothesized mod-
erators had an effect on learning from simulation games relative to the
comparison group (the exception is entertainment value). Trainees in the
simulation game group learned more, relative to the comparison, when
simulation games actively rather than passively conveyed course material,
trainees had unlimited access to the simulation game, and the simulation
game was used as a supplement to other instructional methods rather than
as standalone instruction. The comparison group learned more than the
simulation game group when the comparison group received instruction
as a substitute for the simulation game that actively engaged them in
the learning experience. Furthermore, the extent to which the simulation
game group learned more than the comparison group was greater in pub-
lished than unpublished studies. However, the QWwas signiﬁcant for all
of the moderator results, indicating that there was more variation within
the moderator categories than would be expected by subject-level sam-
pling error alone (Lipsey & Wilson, 2001). That is, none of the moderator
variables independently accounted for all of the variability in the learning
effect sizes across studies.
512 PERSONNEL PSYCHOLOGY
Joint moderator effects. A limitation of the subgroup approach for
examining moderators is that it is restricted to testing individual hy-
potheses and does not control for possible confounds between correlated
moderators (Hedges & Olkin, 1985; Miller, Glick, Wang, & Huber, 1991).
To address this concern, I used WLS regression to test the joint effect of
the moderators on the learning effect sizes. In block one, I controlled for
publication status, given that it was the only methodological factor with a
signiﬁcant moderating effect. In block two, I entered the ﬁve hypothesized
moderators. Only 55 studies provided the information necessary to code
all six moderators so statistical signiﬁcance was interpreted at the .10
level. Publication status accounted for a signiﬁcant 9.8% of the variance
in learning (β=.31; p<.05). After controlling for publication status, the
ﬁve hypothesized moderators accounted for an additional 18.0% of the
variance in learning (p<.10). However, the only moderator with a sig-
niﬁcant main effect was simulation game as the sole instructional method
(β=.28; p<.10). Trainees learned more from simulation games, relative
to the comparison group, when the simulation game was used as a supple-
ment to other instructional methods rather than as stand-alone instruction.
This suggests that some of the observed moderator effects have multiple
determinants, and whether the simulation game is the sole instructional
method may be driving some of the other moderator results. However,
the results also suggest that there is some added utility in considering the
effects of the other hypothesized moderators. After controlling for both
publication status and supplement versus stand-alone instruction, enter-
tainment value, activity level of simulation game instruction, unlimited
access to the simulation game, and the activity levelof comparison groups’
instruction accounted for an additional 5.2% of the variance in learning
effect sizes. Thus, there is some value added by considering the effects of
all of the hypothesized moderators.
Organizations and universities are investing millions of dollars in
computer-based simulation games to train their employees and college
students (Bell et al., 2008; Summers, 2004). Previous reviews of the lit-
erature provide mixed evidence of the extent to which simulation games
are more effective than other instructional methods and have the inher-
ent assumption that the instructional effectiveness of simulation games
is the same for children and adults (e.g., Lee, 1999; O’Neil et al., 2005;
Randel et al., 1992; Vogel et al., 2006). Moreover, many researchers have
speculated as to which features of simulation games and the instructional
context increase trainees’ motivation and learning (e.g., Malone, 1981;
TRACI SITZMANN 513
Tennyson & Jorczak, 2008; Tobias & Fletcher, 2007; Wilson et al., 2009),
but there is little consensus on which features are the most important. This
meta-analysis addressed these debates by examining both how much adult
trainees learn from computer-based simulation games, relative to a com-
parison group, and the instructional and contextual factors that contribute
to higher levels of learning.
Instructional Effectiveness of Simulation Games
The meta-analytic results are favorable regarding the use of simula-
tion games in training. Self-efﬁcacy, declarative knowledge, procedural
knowledge, and retention results all suggest that training outcomes are
superior for trainees taught with simulation games relative to the compar-
ison group. These results provide some support for Garris et al.’s (2002)
input–process–output model and Tennyson and Jorczak’s (2008) interac-
tive cognitive complexity theory. Simulation games may have a positive
effect on these outcomes because they aim to inﬂuence both affective
and cognitive processes. Moreover, repeatedly engaging in the simulation
game cycle may increase trainees’ conﬁdence in their ability to remember
and apply the information taught in training.
Two key simulation game theories—Malone (1981) and Garris et al.
(2002)—propose that the primary beneﬁt of using simulation games in
training is their motivational potential. Thus, it is ironic that a dearth of re-
search has compared posttraining motivation for trainees taught with sim-
ulation games to a comparison group. A number of studies have compared
changes in motivation and other affective outcomes from pre- to posttrain-
ing for trainees taught with simulation games (e.g., Jarmon, Traphagan,
& Mayrath, 2008; Orvis, Horn, & Belanich, 2008; Venkatesh & Speier,
2000), but this research design suffers from numerous internal validity
threats, including history, selection, and maturation (Cook & Campbell,
1979). Also, the use of pre-to-post comparisons may result in an up-
ward bias in effect sizes (Lipsey & Wilson, 2001), leading researchers to
overestimate the effect of simulation games on motivational processes.
Several previous training meta-analyses have examined the effective-
ness of technology-delivered instruction. Comparing these results with
the results of previous meta-analyses provides a comprehensive under-
standing of the value of utilizing technology to deliver training and how
simulation games compare to other forms of technology-delivered in-
struction. Zhao, Lei, Lai, and Tan (2005) compared the effectiveness of
distance education courses (i.e., courses where the instructor and students
are physically separated) to face-to-face courses and found no difference
in the overall effectiveness of the two delivery media. However, several
514 PERSONNEL PSYCHOLOGY
previous meta-analyses have reported positive effect sizes for various
forms of technology-delivered instruction relative to classroom instruc-
tion, including computer-assisted training (Kulik, 1994; Kulik & Kulik,
1991) and hypermedia systems (Liao, 1999). Recently, Sitzmann et al.
(2006) found online instruction was 6% more effective than classroom in-
struction for teaching declarative knowledge, but the two delivery media
were equally effective for teaching procedural knowledge, and trainees
were equally satisﬁed with online and classroom instruction. In addition,
blended learning was 13% more effective than classroom instruction for
teaching declarative knowledge and 20% more effective than classroom
instruction for teaching procedural knowledge, but trainees reacted 6%
more favorably toward classroom instruction. In comparison, this meta-
analysis found trainees in the simulation games group had 11% higher
declarative knowledge levels, 14% higher procedural knowledge levels,
and 9% higher retention levels than trainees in the comparison group.
Furthermore, simulation games were 17% more effective than lecture and
5% more effective than discussion, the two most popular instructional
methods in classroom instruction. Thus, the current results are in line with
the results of other meta-analyses on technology-delivered instruction and
suggest that, when properly employed, technology can enhance learning
The main effect results support the continued investment in simulation
games. However, computer-based simulation games are more expensive
to develop than other forms of technology-delivered training, with com-
plex simulation games costing between $5 and $20 million to create
(Jana, 2006; T+D, 2007). Traditional online training takes an average of
220 hours to create each hour of instructional content, whereas online
simulations require 750 to 1,500 hours to create each hour of instructional
content (Bell et al., 2008; Summers, 2004). However, if some of the as-
sumptions about simulation game play hold true—that a preponderance of
employees will choose to play the simulation game, employees are willing
to devote their free time to playing work-related simulation games, and
simulation games reduce attrition from training (DeRouin-Jessen, 2008;
Garris et al., 2002; Jana, 2006)—organizations may realize that investing
in simulation games is a sound use of their training dollars. Furthermore,
the cost of developing simulation games may be offset by the reduction
in travel costs for training that used to be delivered via classroom instruc-
tion. In order to maximize the utility of simulation games, game designers
need to focus on content reuse or utilizing software that streamlines the
game development process in order to reduce the cost of developing sim-
ulation games. In addition, more research is needed to investigate the
return on investment for simulation games relative to other instructional
TRACI SITZMANN 515
Effect of Simulation Game Design and the Instructional
Situation on Learning
Most simulation game models and review articles propose that the
entertainment value of the instruction is a key feature that inﬂuences
instructional effectiveness (e.g., Garris et al., 2002; Tennyson & Jorczak,
2008; Wilson et al., 2009). Contrary to popular assumption, the empirical
summary of the literature suggests that this feature did not impact learning.
However, whether the simulation game was implemented as stand-
alone instruction or as a supplement to other instructional methods had
a strong effect on learning from simulation games, relative to the com-
parison group. Furthermore, this was the only moderator that had a sig-
niﬁcant effect on learning while controlling for the other hypothesized
moderators. Best practices in simulation game design recommend inte-
grating this instructional method in a program of instruction rather than
as a stand-alone instructional technique (Hays, 2005; Tobias & Fletcher,
2007). Garris et al.’s (2002) theory proposes that a debrieﬁng session after
game play mediates the effect on learning. Simulation games may be an
ineffective stand-alone training tool because people do not naturally learn
complex relationships from experience alone (Garris et al., 2002; Simons,
1993). This is consistent with Dewey’s (1938) assumption that experience
plus reﬂection is required for learning and Hays’ (2005) recommendation
that simulation games should be embedded in an instructional program.
Rarely are simulation games so well designed that trainees can learn an
instructional topic through game play alone (Tennyson & Jorczak, 2008),
and even the best simulation games do not guarantee learning will occur
(Salas, Bowers, & Cannon-Bowers, 1995; Salas, Bowers, & Rhodenizer,
Ensuring that the vast majority of the simulation game content is de-
livered via active, rather than passive, instruction also enhanced learning
from simulation games, relative to the comparison group. Theory suggests
that the advantage of simulation games is that they actively engage trainees
in learning the training material (Garris et al., 2002; Malone, 1981; Ricci
et al., 1996). Utilizing passive instruction in simulation games is contrary
to recommendations for simulation game design (Ricci et al., 1996) and
general best practices in training design (Jonassen, 2002; Northup, 2002;
Sitzmann et al., 2006; Webster & Hackley, 1997). However, about 16%
of the simulation games included in the meta-analysis conveyed the ma-
jority of the instructional content in a passive manner via text or audio
explanations of the course material. This suggests that researchers and
practitioners need to ensure that the instructional experience is accurately
labeled and instruction delivered via simulation games is always actively
conveyed. Otherwise the training is merely conventional instruction that
516 PERSONNEL PSYCHOLOGY
is supplemented with a simulation experience. Thus, simulation game de-
signers must utilize creative techniques for teaching work-related knowl-
edge and skills during game play. This can best be accomplished by
including instructional designers with pedagogical expertise on simula-
tion game development teams (Fletcher & Tobias, 2006). It is critical to
remember that simulation games are just tools for training, and learn-
ing principles must be incorporated in the design of simulation games to
ensure that they are effective learning tools (Salas et al., 1998; Salas &
Learning was also enhanced when trainees could choose to utilize the
simulation game as many times as desired. Extensive time to engage in
game play is essential for the game cycle in Garris et al.’s (2002) model to
occur. There is a cyclical relationship among enjoyment, the decision to
continue playing the simulation game, and system feedback. When limits
are placed on trainees’ interaction with the simulation game, it stunts the
game cycle, thereby limiting the learning potential.
One feature of the comparison group—whether the comparison group
received instruction as a substitute for the content covered in the simula-
tion game and, if so, whether the instruction was active or passive—was
instrumental in determining the relative effectiveness of simulation games.
The extent to which the simulation game group learned more than the com-
parison group was greatest when the comparison group did not receive
an alternative form of instruction as a substitute for game play. However,
learning was greater for the comparison than the simulation games group
when the comparison group was actively engaged in learning the training
material, while the treatment group utilized the simulation game. Consis-
tent with Clark’s (Clark, 1983, 1994) theory and previous meta-analytic
ﬁndings (Sitzmann et al., 2006), this conﬁrms that technology is a means
for delivering training but does not have a direct effect on learning. Rather,
computer-based simulation games and other instructional methods must
actively engage learners in the instructional experience to maximize their
Consistent with previous meta-analyses on psychological interven-
tions (e.g., Lipsey & Wilson, 1993), I also found evidence of publication
bias in the simulation games literature. Publication bias is often referred to
as the “ﬁle drawer problem” and occurs when the probability that a study
is published is dependent on the magnitude, direction, or signiﬁcance of
a study’s results (Begg, 1994). Two previous meta-analyses in this area
(Lee, 1999; Vogel et al., 2006) included a very limited number of unpub-
lished studies. Thus, Vogel et al. may have overestimated the cognitive
gains from simulation games. Furthermore, Lee’s effect size of .41 for
academic achievement may have been greater than the current learning
effect sizes due to the failure to include sufﬁcient unpublished research.
TRACI SITZMANN 517
Accounting for the upward bias in published studies adds credibility to
Recommendations for Practitioners
The results support Salas and Cannon-Bowers’ (2001) notion that it is
misleading to conclude that a simulation game “(in and of itself) leads to
learning” (p. 484). Simulation games should not be employed in training
simply because the technology exists, but rather, careful consideration
is required to determine training needs and which instructional features
should be included in the simulation game (Salas et al., 1998). The en-
tertainment value of simulation games did not affect how much trainees
learned from simulation games relative to the comparison group. Rather,
avoiding teaching trainees with passive instruction during game play was
the simulation game feature that was instrumental in enhancing learning.
Simulation games need to actively engage the learner as they are re-
viewing the instructional material. The comparison group learned more
than the simulation game group when the majority of the material covered
in the simulation game utilized passive instructional techniques. For ex-
ample, DeRouin-Jessen (2008) had trainees maneuver a game character
through a fantasy world. Trainees were active as they maneuvered their
characters, but they were not learning the course material at this point.
Rather, trainees were expected to read a passage or listen to a character
explain the course material once they reached their destination. Utilizing
passive instruction in simulation games violates a fundamental course
design principle—“learning is best accomplished through the active in-
volvement of the students” (Webster & Hackley, 1997, p. 1284).
It is also important to consider the role of the instructional context in
determining how much trainees learn from simulation games. Learning
was maximized from simulation games, relative to the comparison group,
when trainees had unlimited access to the simulation game. As such,
organizations may beneﬁt from providing trainees with a copy of the
simulation game so that they can learn during their free time. Anecdotal
and empirical evidence from Cold Stone Creamery, Canon Inc., and Cisco
Systems suggests that employees are willing to utilize simulation games
on their own time and improve their work-related competencies as a result
of game play (Aldrich, 2007; Jana, 2006). However, to maximize the
potential of computer-based simulation games, they should be used as a
supplement to lecture, discussion, tutorials, or other instructional methods.
Simulation games are beneﬁcial for practicing work-related skills, but
trainees must ﬁrst learn work-related knowledge in order to apply it during
game play. Moreover, a debrieﬁng session after game play is beneﬁcial
for ensuring that trainees realize how their experience in the simulation
518 PERSONNEL PSYCHOLOGY
game is applicable to the work environment (Garris et al., 2002; Salas &
Study Limitations and Directions for Future Research
Consistent with Wilson et al. (2009) and the limitations noted in previ-
ous meta-analyses (e.g., Sitzmann, Ely, Brown, & Bauer, 2010), I found the
level of detail reported in primary research restricted which attributes of
simulation games and the instructional context could be meta-analytically
examined. Detailed descriptions of the training course and instructional
situation would have enabled coding whether there was a debrieﬁng ses-
sion at the end of the simulation game as well as whether the training goals
were salient to trainees during game play. It would also have enabled cod-
ing some of the moderators on a continuous rather than dichotomous scale,
increasing the variability across studies in the moderator effects. In addi-
tion, psychological ﬁdelity was not included in the meta-analysis due to
its high correlation with entertainment value. However, Vogel et al. (2006)
found the level of picture realism did not moderate the effectiveness of
simulation games. Future research should include detailed descriptions of
the training courses and instructional context in order to advance meta-
Moreover, the hypothesized moderators were interrelated such that
simulation games that were well designed tended to implement multiple
hypothesized moderators. This is a common problem in meta-analytic
research because studies that implement certain instructional features are
likely to have other co-occurring features (Lipsey, 2003). These interre-
lationships represent statistical confounds that make it difﬁcult to tease
apart of the role of a single moderator. In this meta-analysis, when the
hypothesized moderators were considered jointly, whether the simulation
game was implemented as stand-alone instruction or a supplement to
other instructional methods was the only hypothesized moderator that had
a signiﬁcant effect on learning. Although the other moderator effects were
not signiﬁcant, follow-up analyses revealed that they had a meaningful
inﬂuence on the effect size, after controlling for publication status and
supplement/stand alone. Additional primary research is needed to exam-
ine the joint and independent effects of characteristics of simulation games
and the instructional context on affective, behavioral, and cognitive train-
ing outcomes. This research should investigate the effects of each of the
components of entertainment value (e.g., challenge and fantasy) as well
as other course design features (e.g., learner control, feedback, adaptive
guidance), which previous research has demonstrated inﬂuence the ef-
fectiveness of technology-delivered instruction (Bell & Kozlowski, 2002;
Kraiger & Jerden, 2007; Sitzmann et al., 2006). Researchers should also
TRACI SITZMANN 519
examine whether the amount of time that trainees spend reviewing with
simulation games, rather than the level of access that they have, moderates
the effectiveness of game play.
Another limitation is that there was insufﬁcient primary research on
simulation games to run the moderator analyses separately for the three
learning outcomes. It is possible that the moderator variables may have
different effects on these three outcomes. In order to overcome this limi-
tation, additional primary research is needed on the effectiveness of sim-
ulation games. In addition, only one study was identiﬁed that compared
posttraining motivation for trainees taught with simulation games to a
comparison group (DeRouin-Jessen, 2008); two studies compared effort
exerted (DeRouin-Jessen, 2008; Sukhai, 2005); three studies compared
trainee reactions (DeRouin-Jessen, 2008; Parchman et al., 2000; Ricci
et al., 1996); and one study compared transfer (Meyers et al., 1989). This
is a monumental gap in our collective understanding of the effectiveness
of simulation games and suggests the need for additional research on the
affective and skill-based outcomes of simulation games, relative to other
instructional methods. This research should randomly assign trainees to
experimental conditions in order to eliminate potential confounds (e.g.,
trainee motivation) that may inﬂuence the level of effort that trainees exert,
their satisfaction with the instruction, or their training transfer.
Hays (2005) proposed that simulation games are motivational, but they
motivate trainees to play the game rather than to enhance their work-related
knowledge and skills. Anecdotal evidence partially supports this claim—
adults get absorbed when playing their favorite games and experience a
loss of time when engaged in game play (Wood et al., 2007). However,
the instructional beneﬁts of simulation games would be maximized if
trainees were also motivated to utilize the knowledge and skills taught in
simulation games on the job. Conﬁrming that simulation games enhance
work-related motivation is a critical area for future research.
Research is also needed to assess the level of cognitive load imposed
by simulation games and techniques for ensuring that the cognitive load
does not exceed the cognitive capacity of trainees. Simulation games tend
to utilize discovery learning environments (Munro, 2008), and both Mayer
(2004) and Sweller (1999, 2004) have acknowledged that discovery learn-
ing imposes a heavy cognitive load on trainees. Simulation game players
must make numerous choices, recall game rules, and develop simula-
tion game strategies while also increasing their work-related knowledge
(Tobias & Fletcher, 2007). Researchers should investigate how guidance
and advanced organizers can be incorporated in simulation games to
increase instructional effectiveness and minimize the cognitive load of
training. Furthermore, active learning theory suggests that core training
design elements (e.g., exploration and training frame) directly inﬂuence
520 PERSONNEL PSYCHOLOGY
self-regulatory processes and indirectly inﬂuence adaptive transfer (Bell
& Kozlowski, 2008). It is possible that incorporating these design ele-
ments in simulation games will enhance training effectiveness, and this is
an essential avenue for future research.
Additional research is also needed to examine the utility of other
advanced training technologies. For example, how can organizations in-
corporate virtual worlds in training to enhance learning outcomes? Fur-
thermore, peer production of training content (e.g., Youtube, Wikipedia)
is becoming increasingly commonplace (Brown & Sitzmann, 2011). How
can an organization ensure that the information exchanged is accurate
and fosters the organization’s best interest? Finally, intelligent tutoring
systems are becoming more practical to develop and deploy. Research is
needed to examine whether trainees feel the same level of connection with
an intelligent tutor as with a human tutor such that they will engage in
the conversation and reach a deeper level of understanding of the training
material (Sitzmann & Ely, 2008).
Simulation games have the potential to enhance the learning of work-
related knowledge and skills. Overall, declarative knowledge was 11%
higher for trainees taught with simulation games than a comparison group;
procedural knowledge was 14% higher; retention was 9% higher; and
self-efﬁcacy was 20% higher. Characteristics of simulation games and
the instructional context were instrumental in determining the amount
that trainees learned from simulation games relative to a comparison
group. Speciﬁcally, learning from simulation games was maximized when
trainees actively rather than passively learned work-related competencies
during game play, trainees could choose to play as many times as desired,
and simulation games were embedded in an instructional program rather
than serving as stand-alone instruction. The ultimate goal for simulation
game design teams is to exploit the motivational capacity of simula-
tion games to enhance employees’ work-related skills. Thus, additional
research is needed to examine the dynamic interplay of affective and cog-
nitive processes during game play and, ultimately, their effect on training
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