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Facts are more important than novelty: Replication in the educational sciences

Educational Researcher
The online version of this article can be found at:
DOI: 10.3102/0013189X14545513
published online 13 August 2014EDUCATIONAL RESEARCHER
Matthew C. Makel and Jonathan A. Plucker
Facts Are More Important Than Novelty: Replication in the Education Sciences
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Educational Researcher, Vol. XX No. X, pp. 1 –13
DOI: 10.3102/0013189X14545513
© 2014 AERA.
arl Sagan (1997) once wrote,
At the heart of science is an essential balance between two
seemingly contradictory attitudes—an openness to new ideas, no
matter how bizarre or counterintuitive they may be, and the most
ruthless skeptical scrutiny of all ideas, old and new. This is how
deep truths are winnowed from deep nonsense. (p. 304)
The desire to differentiate “truth from nonsense” has been a con-
stant struggle within science, and the education sciences are no
exception. Over the past decade, these efforts have been espe-
cially pronounced at the federal level, with the creation of the
Institute for Education Sciences (IES) within the U.S.
Department of Education. More to the point, IES funded the
creation of the What Works Clearinghouse (WWC) in 2002 to
serve as a “central and trusted source of scientific evidence for
what works in education” (WWC, 2013). Similar, alternative
resources have been created, including the Doing What Works
website, which draws largely from the WWC, and the IES-
supported Center for Data-Driven Reform in Educations Best
Evidence Encyclopedia.1
The efforts of these and related initiatives rely heavily on ran-
domized controlled trials (RCTs) and meta-analyses. For exam-
ple, although the structure and activities of the WWC have
evolved over time, a RCT design was originally required for
inclusion in the WWC. RCTs were colloquially deemed the
“gold standard” in education research by IES (e.g., Whitehurst,
2003). In a similar vein, a National Reading Panel (1999) report
To make a determination that any instructional practice could be
or should be adopted widely to improve reading achievement
requires that the belief, assumption, or claim supporting the
practice can be causally linked to a particular outcome. The
highest standard of evidence for such a claim is the experimental
study, in which it is shown that treatment can make such changes
and effect such outcomes. (p. 1-7)
Of course, RCTs—and even meta-analyses of RCTs—have
their limitations, as do all approaches to research. Problems that
can tarnish the gold standard include, but are not limited to, bias
toward publishing positive findings (e.g., Bakker, van Dijk, &
Wicherts, 2012; Bozarth & Roberts, 1972; Fanelli, 2010, 2012;
Pigott, Valentine, Polanin, Williams, & Canada, 2013; Sterling,
1959; Sterling, Rosenbaum, & Weinkam, 1995), low reliability
among peer reviewers (e.g., Cicchetti, 1991; Cole, 1992; D. Peters
& Ceci, 1982), hypothesizing after the results are known (e.g.,
N. Kerr, 1998; Makel, 2014; Maxwell, 2004), misuse of statistical
tests and results (e.g., Bakker & Wicherts, 2011: Kruskal &
545513EDRXXX10.3102/0013189X14545513Educational ResearcherMonth XXXX
1Duke University, Durham, NC
2University of Connecticut, Storrs, CT
Facts Are More Important Than Novelty:
Replication in the Education Sciences
Matthew C. Makel1 and Jonathan A. Plucker2
Despite increased attention to methodological rigor in education research, the field has focused heavily on experimental
design and not on the merit of replicating important results. The present study analyzed the complete publication history of
the current top 100 education journals ranked by 5-year impact factor and found that only 0.13% of education articles were
replications. Contrary to previous findings in medicine, but similar to psychology, the majority of education replications
successfully replicated the original studies. However, replications were significantly less likely to be successful when there
was no overlap in authorship between the original and replicating articles. The results emphasize the importance of third-
party, direct replications in helping education research improve its ability to shape education policy and practice.
Keywords: assessment; content analysis; educational policy; evaluation; replication; research methodology
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Majors, 1989), the file drawer problem of nonpublished studies
that arrive at negative or mixed findings (e.g., Rosenthal, 1979;
Rotton, Foos, Vanmeek, & Levitt, 1995; Spellman, 2012), the
decline effect from large initial findings (e.g., Ioannidis, 2005a;
Rhine, 1934/1997; Schooler, 2011), overreliance on null hypoth-
esis testing (e.g., Bakan, 1966; Cohen, 1994; Johnson, 2013;
LeBel & Peters, 2011; Lykken, 1968; Rozeboom, 1960;
Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011),
experimenter/researcher degrees of freedom (e.g., Rosenthal,
1966, 1967; Simmons, Nelson, & Simonsohn, 2011), and data
peeking (e.g., John, Loewenstein, & Prelec, 2012; Wagenmakers,
Wetzels, Borsboom, van der Maas, & Kievit, 2012).
Given the potential weaknesses of experimental designs, there
is ample evidence of the need to go beyond the gold standard.
Indeed, Campbell and Stanley (1963), in their seminal chapter
on the use of experimental designs in education research, cau-
tioned against considering true experiments to be a panacea, sug-
gesting instead that they be viewed as a path to accumulating
One additional avenue to accumulate understanding is repli-
cation. Broadly, replication is the purposeful repetition of previ-
ous research to corroborate or disconfirm the previous results.
Replications also comprise the research used to compose meta-
analyses. However, it is important to note that meta-analyses are
not the same as replications (Makel & Plucker, 2014). Replication
is necessary for meta-analysis, but meta-analyses can be based on
studies with quite varied purposes. For example, a meta-analysis
on the effects of academic acceleration could rely on studies
investigating grade skipping and early entrance into kindergar-
ten even though the individual studies would not be considered
replications of each other. Thus, studies may come from the
same meta-analytic pool but not serve the same purpose. Meta-
analyses synthesize previous research, whereas replications seek
to verify whether previous research findings are accurate.
Additionally, meta-analyses do not easily account for biases
against reporting all outcomes (see Pigott et al., 2013), whereas
such biases can be uncovered via replication.
Other fields within the social sciences, most notably, psychol-
ogy, in recent years have found that replication not only helps
pave the path to understanding but also serves as a way to reveal
fraud. In this paper, we review classic and contemporary think-
ing about replication, note current debates about replication in
other fields, and provide data on the current state of replication
in the education sciences.
Conceptions of Replication
Despite being one of the basic building blocks of science, there
is no universally agreed upon list of necessary and sufficient fea-
tures of a replication (for an overview, see Schmidt, 2009). In a
classic paper on how to interpret statistical significance, Lykken
(1968) stated that researchers “are interested in the construct . .
. not in the datum” (p. 156). To organize these interests, he pro-
posed three types of replications: literal, operational, and con-
structive. Literal replications “involve exact duplication of the
first investigator’s sampling procedure, experimental conditions,
measuring techniques, and methods of analysis” (Lykken, 1968,
p. 155). This form of replication basically calls for the original
investigator to collect more data from additional participants in
a consistent manner; literal replications are often considered to
be impossible (Hansen, 2011; Lindsay & Ehrenberg, 1993;
Madden, Easley, & Dunn, 1995) or to suffer from the very same
experimenter bias that replications attempt to address.
Operational replications are when the researcher “strives to
duplicate exactly just the sampling and experimental procedures”
(Lykken, 1968, p. 155). Independent researchers can (hopefully)
follow the same “experimental recipe” from the original Methods
section. Finally, in constructive replications,
one deliberately avoids imitation of the first author’s methods. . . .
One would provide a competent investigator with nothing more
than a clear statement of the empirical “fact” which the first author
would claim to have established . . . and then let the replicator
formulate his own methods of sampling, measurement, and data
analysis. (Lykken, 1968, pp. 155-156; italics in the original)
Operational replications test the veracity of the original data,
whereas constructive replications test the targeted construct.
A recent review by Schmidt (2009) connects replication the-
ory with replication in practice. Schmidt lists five functions rep-
lications serve: to control for sampling error, to control for
artifacts, to control for fraud, to generalize to different/larger
populations, or to assess the general hypothesis of a previous
study. Rather than deliberately avoiding the original methods,
Schmidt suggests systematically changing individual facets of the
original study to better understand its nature. The review also
reframed replication into direct and conceptual replications.
Similar to Lykken’s (1968) first two replication types, direct rep-
lications repeat the experimental procedure, whereas conceptual
replications use different methods to test the underlying hypoth-
esis. We use Schmidt’s conceptualization in this paper.
The relative importance of direct and conceptual replications
has been debated. Some scholars argue that conceptual replica-
tion should be emphasized (e.g., Levy, 1969; Loevinger, 1968;
Ostrom, 1971; Smith, as cited in Yong, 2012), while others sup-
port direct replications (e.g., LeBel & Peters, 2011; Ritchie,
Wiseman, & French, 2012). The importance of each depends on
the goal of the investigation (Jones, Derby, & Schmidlin, 2010;
La Sorte, 1972; Rosenthal, 1969), with direct replication typi-
cally seeking to verify or corroborate the original findings using
the same methods as the original researchers; conceptual replica-
tions test more general models and theories. However, it is impor-
tant to note that only direct replications can disconfirm or
corroborate previous claims. This is because a failed conceptual
replication does not automatically identify a flaw in the original
study but instead has the potential to identify the generalizability
(or lack thereof) of the original finding. Direct replication, on the
other hand, can help identify potential biases in the original study
or confirm that the original finding was not an anomaly. Because
of this, some argue direct replication should always precede con-
ceptual replication attempts (e.g., Pashler & Harris, 2012).
Why Not Replication?
Replication research can help identify, diagnose, and minimize
many of the methodological biases listed above, with Collins
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(1985) going so far as to call replication the Supreme Court of
science. Despite the benefits that replication brings to the
research table, conducting replications is largely viewed in the
social science research community as lacking prestige, original-
ity, or excitement (Lindsay & Ehrenberg, 1993; Neuliep &
Crandall, 1993b), a bias that is not always shared in the natural
sciences (Madden et al., 1995, but cf. Bissell, 2013). Several
recent publications have begun to discuss the hurdles and disin-
centives to conducting replications that appear to be endemic to
the social science research infrastructure (e.g., Carpenter, 2012;
Hartshome & Schachner, 2012; Makel, 2014; Makel & Plucker,
2014; Schmidt, 2009; Spellman, 2012).
For example, some posit that “successful replications are
unpublishable; journals reject such research saying ‘but we
already knew that’” (Spellman, 2012, p. 58). Such systemic
biases are well established and include the following:2
1. Submission bias. Conducting research and submitting
for publication is time-consuming, and investigators
may purposefully remove replications from the publica-
tion process to focus on other projects or because they
believe replications cannot be published (e.g., Schlosberg,
1951; Spellman, 2012).
2. Funding bias. Research, including and especially RCTs,
requires resources, making replications difficult to con-
duct if not funded (e.g., Schmidt, 2009).
3. Editor/reviewer bias. Journal editors and reviewers may
be more likely to reject replications, driven by an implicit
(or even explicit) belief that replications are not as pres-
tigious as nonreplication articles (e.g., Makel, 2014;
Neuliep & Crandall, 1990, 1993a, 1993b; Smith, as
cited in Yong, 2012).
4. Journal publication policy bias. Journals may have poli-
cies against publishing replications (e.g., Madden et al.,
1995; Ritchie et al., 2012; Smith, as cited in Yong, 2012).
5. Hiring bias. Institutions may not hire researchers who
conduct replications, with Biases 2 and 3 possibly play-
ing a role in these decisions.
6. Promotion bias. Similar to hiring bias, organizations
may not value replication research as favorably as new
and groundbreaking research within promotion and ten-
ure activities (e.g., Madden et al., 1995).
7. Journals-analyzed bias. Previous research analyzing repli-
cation rates may have selected journals that publish few
replications. Because each journal has its own editorial
policies, it may be that some journals are more likely to
accept replications than others (e.g., Ritchie et al., 2012).
8. Novelty equals creativity bias. Editors, reviewers, and
researchers value creative contributions, but novelty and
creativity are not synonymous. Most definitions of cre-
ativity and innovation propose criteria of novelty and
utility; a novel result that cannot be replicated is by defi-
nition not creative (e.g., Makel & Plucker, 2014; Plucker,
Beghetto, & Dow, 2004).
These biases may not uniformly deny publication of replica-
tions, but they certainly impede the process. Perhaps the most
baffling aspect is that these biases exist even though the call for
replications has existed for generations (e.g., Ahlgren, 1969;
Bozarth & Roberts, 1972; Cohen, 1994; Rosenthal, 1969;
Tukey, 1969).3 Indeed, the incongruity between need and action
has not gone unnoticed. Furchtgott (1984), in a discussion of
the need to alter the outlook on publishing replications, stated
that “not only will this have an impact on investigations that are
undertaken, but it will reduce the space devoted to the repeti-
tious pleas to replicate experiments” (p. 1316).
Replication in Other Scientific Domains
The concern over replication exists in many research domains,
including advertising (Madden et al., 1995), biology (Begley &
Ellis, 2012; Powell, 2007), economics (Anderson, Greene,
McCullough, & Vinod, 2005; Dewald, Thursby, & Anderson,
1986; Kane, 1984), library sciences (Winters, 1996), marketing
(Evanschitzky, Baumgarth, Hubbard, & Armstrong, 2007;
Hubbard & Armstrong, 1994), medicine (Ioannidis, 2005a,
2005b, 2005c), political science (Golden, 1995; King, 1995),
public health (Valentine et al., 2011), and sociology (La Sorte,
1972). A December 2011 special section in Science (Jasny, Chin,
Chong, & Vignieri, 2011) discussed replication and its applica-
tion in primate cognition (Tomasello & Call, 2011), field biol-
ogy (Ryan, 2011), computer science (Peng, 2011), and genomics
(Ioannidis & Khoury, 2011).
Using health care research as an example, Ioannidis (2005a),
in a review of highly cited medical publications (i.e., those cited
more than 1,000 times), found only 44% of replications pro-
duced results similar to the original study. Unsuccessful replica-
tions were most common when the original studies were not
randomized and had small samples, both of which are common
features of education research (especially compared to clinical
medical research). In a separate study attempting to replicate
highly cited cancer trial studies, researchers were able to success-
fully replicate only 6 of 53 trials, a success rate of just over 11%
(Begley & Ellis, 2012). Similarly, researchers from the Bayer drug
company were able to replicate only 35% of the published
research findings they analyzed (Prinz, Schlange, & Asadullah,
2011). With such low replication success rates in domains known
for methodological rigor and large samples sizes, the need for rep-
lication within the social sciences becomes more acute.
Replication has received the most attention of late in psychol-
ogy (e.g., Perspectives on Psychological Science special issue, 2012;
Yong, 2012). Recent conversations in psychological science over
the publication of a controversial study on extrasensory percep-
tion (Bem, 2011) along with a few well-publicized cases of fraud
(by eminent researchers) have energized discussion around what
can be done to increase confidence in research findings.
Moreover, the rate at which researchers accurately predict the
outcomes of their own studies appears to support Bem’s (2011)
findings that precognition exists (e.g., Fanelli, 2010, 2012;
Sterling, 1959; Sterling et al., 1995). Fanelli (2010) found that
91.5% of psychology studies supported the predicted outcome,
making psychologists nearly 5 times better at predicting results
than actual rocket scientists (i.e., space scientists). Moreover, the
91.5% success rate actually represents a decrease from the 97%
success rate reported by Sterling (1959). Simmons and col-
leagues (2011) note that this hyperaccurate prediction record is
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probably due to several factors, including collecting data until
the desired result is found, not reporting unsuccessful trials, and
eliminating observations and variables post hoc that do not sup-
port the targeted hypotheses. Nevertheless, according to a recent
simulation study, publishing all studies—not just those that are
statistically significant—leads to more accurate estimates of
actual effects and differences (de Winter & Happee, 2013).
Data on replication rates are available for a few scientific
fields. Makel, Plucker, and Hegarty (2012) analyzed the com-
plete publication history of the 100 psychology journals with the
highest 5-year impact factors and reported that only 1.07% of
psychology publications were replications. Moreover, they noted
the rate at which replications are being published is rising, albeit
slowly (roughly 2% of publications since 2000 compared to less
than 1% of publications in the 1980s and earlier). Contrary to
the failure to replicate results in the medical field, less than 10%
of psychology replications failed to replicate previous findings.
In a similar analysis of marketing research journals, Evanschitzky
et al. (2007) analyzed nearly 1,400 articles from 1990 to 2004,
and Hubbard and Armstrong (1994) analyzed the same journals
from 1974 to 1989. Nearly 2.5% of the articles from 1974 to
1989 were replications, compared to 1.2% of articles from 1990
to 2004. This trend suggests a decrease in replication research,
despite the fact that both studies found that the majority of
those studies failed to replicate the original research!
Nevertheless, the concern about a dearth of replications is not
universal. One journal editor claims, “I would wager a year’s
associate-editor pay that most [Academy of Management Journal]
articles include at least partial replication, albeit not exact and, of
course, not labeled ‘replication research’” (Eden, 2002, p. 842).
Similarly, an analysis of communication journals reported that
28% of studies had some form of replication, but only 3%
clearly identified themselves as such (Kelly, Chase, & Tucker,
1979). This kind of masking is expected when the contents of
rejection letters say things like a replication “translates into a
minimal contribution to the field” (Sterling et al., 1995, p. 109).
Similarly, 52% of surveyed social science editors reported that
being a replication contributes to being rejected for publication.
In fact, the only factors associated more strongly with rejection
were the paper being published in the proceedings of a national
(61%) or regional (53%) conference and an experiment that did
not have a control group (54%; S. Kerr, Tolliver, & Petree,
1977). With such a high rejection rate, the disincentives to
attempt replications are considerable. With the obvious lack of
replicability in the medical studies discussed above, the concern
over the veracity of some bedrock empirical beliefs should be
high, making a lack of published replications a major weakness
in any empirical field.
Replications in Education Research
The first use of the term replication in an education journal
appeared in a 1938 paper in the Journal of Educational Research
titled “An Example of Replication of an Experiment for Increased
Reliability” (C. Peters, 1938). Focusing on the importance of
relying on more than one implementation of an experiment, C.
Peters (1938) emphasized the importance of relying on indepen-
dent tests to understand the reliability of a particular finding
(e.g., does one teaching method lead to better performance than
another?). Given the current conversation regarding the impor-
tance of replication, the paper closes with great prescience,
It is best not to place much confidence in a mathematically
inferred ratio as far as its exact size is concerned but to stop with
the assurance that a set of differences prevailing in the same
direction indicates greater reliability than that expressed by the
ratios of the samples taken singly. (C. Peters, 1938, p. 9)
Like many of the domains listed above, education research
has several examples of notable replications, including recent
research on paying students for performance (e.g., Fryer, 2011;
Levitt, List, Neckermann, & Sadoff, 2011) as well as the impact
of merit pay on teachers (e.g., Fryer, 2013; Yuan et al., 2013).
Numerous studies have been conducted on each of these ideas
and are not considered redundant or lacking in value.4 However,
to our knowledge, there have been no systematic investigations
of replication in educational research. The current study applies
the replication lens to education research by providing an over-
view of replications rates in education research journals. If the
biases against replications in other fields extend to educational
research, one would expect that replications in education would
be extremely rare. Three broad sets of questions drove our inves-
tigation. First, how many replications are being published, and is
the number of published replications changing over time?
Second, what types of replications are being conducted; are
direct or conceptual replications being conducted, and are they
being conducted by the authors who conducted the original
research or by a unique team? Finally, we investigated the extent
to which the original findings were successfully replicated.
The top 100 education journals (all types) according to 5-year
impact factors were gathered using the online search engine ISI
Web of Knowledge Journal Citation Reports, Social Sciences
Edition (2011). In January 2013, using Web of Knowledge, the
entire publication history of each of these 100 journals was
searched to identify the total number of articles published as well
as the number of articles that contained the search term replicat*
in the text. This method is similar to what has previously been
used when searching the publication histories of large databases
(e.g., Fanelli, 2010, 2012; Makel et al., 2012).
To estimate the actual replication rate (i.e., the percentage of
articles that are replications), all of the articles that used the term
replicat* were analyzed. This analysis assessed (a) whether the
term was used in the context of a new replication being con-
ducted (as opposed to referring to gene replication) and, if so,
(b) whether it was a direct or conceptual replication, (c) whether
the replication was considered a success or a failure (success
meaning the replicating authors conclude that their findings are
similar to, or in the same direction as, the original findings), (d)
whether the replicated article was written by the same authors
(defined as having an overlap of at least one author), and (e)
whether it was published in the same journal. The number of
times the replicating and replicated articles have been cited were
also recorded in April 2013 (if multiple studies were being
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replicated, the average citation count of the replicated studies
was calculated; the citation counts of books were not recorded
because they are not calculated by Web of Knowledge).
All of the data were collected by the first author. The second
author was given a set of written instructions (similar to the
paragraphs above) to score a randomly selected subset of articles.
In 18 out of 20 cases, articles were coded similarly, with minor,
resolvable differences on the two remaining papers. This process
provided evidence that the method identifying replications is
itself replicable. The articles using replicat* were then split and
independently coded by the authors.
Gain ratios are also reported to help communicate changes in
replication rates. The gain ratio statistic is similar to an odds ratio,
but rather than being based on odds, it is based on the probability
of an outcome (Agresti, 2007). If the probability of two events is
equal (e.g., flipping a coin and getting heads vs. getting tails), the
gain ratio is 1.0 and is considered significantly different from 1.0
if its 95% confidence interval does not include 1.0.
The average 5-year impact factor of the top 100 journals in edu-
cation was 1.55 (range = 0.52 to 5.46). Overall, 461 out of
164,589 articles from these education journals contained the
term replicat*, with 18 journals never using the term. Of the arti-
cles containing replicat*, 221 (47.9%) were actual replications,
giving an overall replication rate of 0.13% (221 out of 164,589;
see Table 1 for a breakdown by journal) for the field. As a com-
parison, the estimated replication rate in psychology (Makel et
al., 2012) was eight times (95% CI [6.99, 9.17]) higher than the
replication rate in education journals. Only six journals had a
replication rate over 1%, and 43 journals published no replica-
tions. Within the current sample, there does not appear to be a
relationship between 5-year impact factor rank and replication
rate (r = –.03, p = .795), although it should be noted that several
of the journals analyzed were review journals that typically do not
publish articles featuring new data. There may be journals outside
the top 100 5-year impact factor that are publishing replications.5
However, if this is the case, we worry that replications are being
relegated to such low-visibility outlets that their ability to impact
subsequent work is severely stunted (i.e., the 5-year impact factor
of the 100th ranked journal was 0.52).
The articles that used the term replicat* but were not actual
replications typically used the term in the context of stating that
the results needed to be replicated or in terms of replicating les-
son plans. It should be noted that 12.7% of replications were
replicating a finding from within the same (usually multistudy)
article. Although not lacking value, within-article replications do
not combat potential experimenter bias, error, or fraud.
As shown in Table 2, of the articles that were determined to be
actual replications, 69.2% were conceptual, 28.5% were direct,
and 2.3% included facets of both (i.e., usually in multistudy
papers). Regarding outcomes, 67.4% of the replications reported
successfully replicating the findings of the original study, 19.5%
had mixed findings (supporting some, but not all, findings), and
13.1% failed to replicate the original findings. Interestingly, com-
parison of success rates by type of replication revealed that 71.4%
of direct replications were successful compared to 66% of
conceptual replications, with direct replications trending more
successful but not significantly so, χ2(4) = 5.95, p = .203, Cramer’s
V = .12.
Only 30.6% of replications of previously published research
were published in the same journal as the original study (replica-
tions from within the same article were not included for this
calculation). But more interestingly, nearly half (48.2%) of the
replications were conducted by the same research team that pub-
lished the original research. The success rates of replications were
significantly different based on whether there was author over-
lap; when replications were in the same publication as the origi-
nal findings, 88.7% of replications were successful. When
replications were in a new publication, but at least one author
was on both the original and replicating articles, 70.6% of repli-
cations were successful. However, when there was no author
overlap, only 54% of replications were successful, χ2(4) = 21.03,
p < .001, Cramer’s V = .22. Although same-author replications
certainly contribute to research knowledge, this type of replica-
tion may not account for potential experimenter bias (regardless
of whether the bias is intentional or unintentional). It is also
worth noting that the recent, high-profile fraud cases within psy-
chology often involved researchers replicating their own fraudu-
lent studies with fraudulent replication data.
As can been seen in Figure 1, the rate at which replications are
being conducted has increased in the last few decades. Since
1990, the replication rate has been 3.92 times higher (95% CI
[2.75, 5.58]) than in previous years. Put another way, replica-
tions have gone from being 1 out of every 2,000 education arti-
cles to being roughly 1 out of every 500.
The median citation count of the replication articles was 5
(range = 0 to 135), whereas the median for the articles being
replicated was 31 (range = 1 to 7,644) times. The original arti-
cles have had more time to be cited because they are older than
their replicating counterparts (median publication year of 1991
and 1998, respectively6), but 5 citations are hardly insignificant
given that only 1 of the top 100 education journals has a 5-year
impact factor higher than 5.
The present study analyzed the publication histories of the educa-
tion journals with the top 100 five-year impact factors and found
0.13% of education publications were replications, substantially
lower than the replication rates of previously analyzed domains.
Contrary to previous findings in medical fields (e.g., Begley &
Ellis, 2012; Ioannidis, 2005c), but similar to psychology research
(Makel et al., 2012), the majority (67.4%) of education replica-
tions successfully replicated the original studies. However, repli-
cations were significantly less likely to be successful when there
was no overlap in authorship between the original and replicating
articles. This difference raises questions regarding potential biases
in replicating one’s own work and may be related to previous
findings of questionable research practices in the social sciences
(John et al., 2012; Simmons et al., 2011). More optimistically,
same-author replications could merely be benefiting from the
wisdom/experience from having done the study previously and
thus may be able to more closely replicate the original methods.
This phenomenon needs additional investigation.
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Table 1
Replication Rates from the Top 100 Journals in Education Research
Journal Title
No. of
Academic Psychiatry 1.05 64 1,296 2 0 0%
Academy of Management Learning & Education 4.05 2 693 0 0 0%
Adult Education Quarterly 0.62 94 901 3 3 0.33%
Advances in Health Sciences Education 2.06 22 635 7 3 0.47%
AIDS Education and Prevention 2.21 20 1,305 18 4 0.31%
American Educational Research Journal 3.09 5 1,953 13 10 0.51%
American Journal of Education 1.16 59 1,117 1 0 0%
Anthropology & Education Quarterly 0.74 85 1,311 1 0 0%
Applied Measurement in Education 0.85 81 373 6 2 0.54%
Australian Educational Researcher 0.52 100 312 0 0 0%
British Educational Research Journal 1.56 40 1,039 4 1 0.10%
British Journal of Educational Studies 1.17 57 2,869 2 0 0%
British Journal of Educational Technology 1.91 28 3,085 4 2 0.06%
British Journal of Sociology of Education 1.17 56 1,459 1 0 0%
Comparative Education 0.86 80 1,859 0 0 0%
Comparative Education Review 1.04 65 2,870 0 0 0%
Computers & Education 2.97 8 3,070 10 3 0.10%
Curriculum Inquiry 0.59 95 1,141 2 0 0%
Early Childhood Research Quarterly 2.61 11 678 13 7 1.03%
Economics of Education Review 1.44 45 1,266 2 1 0.08%
Education and Training in Developmental Disabilities 1.21 53 270 2 0 0%
Education and Urban Society 0.54 99 1,336 0 0 0%
Educational Administration Quarterly 1.39 48 1,390 5 3 0.22%
Educational Assessment Evaluation and Accountability 0.69 89 23 0 0 0%
Educational Evaluation and Policy Analysis 1.81 31 463 4 1 0.22%
Educational Gerontology 0.55 97 2,736 13 8 0.29%
Educational Policy 0.68 91 871 2 0 0%
Educational Research 0.93 74 355 3 2 0.56%
Educational Review 0.99 69 3,360 3 1 0.03%
Educational Studies 0.64 93 1,356 2 1 0.07%
Educational Technology Research and Development 1.65 38 7,280 9 4 0.05%
Elementary School Journal 1.51 41 963 4 4 0.42%
European Physical Education Review 0.77 84 161 1 0 0%
Foreign Language Annals 0.68 92 2,021 7 5 0.25%
Gender and Education 0.90 77 815 1 0 0%
Health Education Research 2.57 14 1,653 17 4 0.24%
Higher Education 1.31 51 3,081 3 2 0.06%
IEEE Transactions on Learning Technologies 0.93 73 166 1 0 0%
Innovations in Education and Teaching International 1.01 66 442 3 2 0.45%
Instructional Science 1.96 24 1,015 7 6 0.59%
Interactive Learning Environments 1.17 58 216 1 0 0%
International Journal of Computer-Supported Collaborative
3.00 7 174 1 0 0%
International Journal of Educational Development 0.93 71 1,482 3 1 0.07%
International Journal of Science Education 1.72 35 2,052 10 6 0.29%
Journal of Adolescent & Adult Literacy 0.72 86 2,512 1 0 0%
Journal of American College Health 2.29 18 1,729 10 4 0.23%
Journal of College Student Development 1.18 55 4,483 10 7 0.16%
Journal of Computer Assisted Learning 1.76 33 771 7 2 0.26%
Journal of Curriculum Studies 0.97 70 2,292 0 0 0%
Journal of Diversity in Higher Education 0.87 79 18 2 1 5.56%
Journal of Education Policy 1.23 52 786 2 0 0%
Journal of Educational and Behavioral Statistics 2.44 16 464 2 0 0%
Journal of Educational Research 1.49 44 7,758 23 17 0.22%
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Journal Title
No. of
Journal of Engineering Education 2.02 23 728 0 0 0%
Journal of Experimental Education 1.64 39 2,718 22 10 0.37%
Journal of Geography in Higher Education 1.42 46 1,141 2 0 0%
Journal of Higher Education 1.79 32 7,749 4 3 0.04%
Journal of Literacy Research 1.09 63 367 3 2 0.54%
Journal of Moral Education 0.72 87 1,715 4 2 0.12%
Journal of Philosophy of Education 0.56 96 819 0 0 0%
Journal of Research in Reading 1.50 43 294 7 4 1.36%
Journal of Research in Science Teaching 2.92 9 2,327 14 8 0.34%
Journal of School Health 1.91 27 5,784 11 4 0.07%
Journal of Social Work Education 1.11 61 1,711 9 3 0.18%
Journal of Teacher Education 2.23 19 3,903 1 0 0%
Journal of Teaching in Physical Education 1.41 47 678 0 0 0%
Journal of the Learning Sciences 3.08 6 347 3 1 0.29%
Language Learning 1.83 29 1,393 13 6 0.43%
Language Learning & Technology 2.47 15 288 0 0 0%
Language Teaching Research 0.91 76 243 1 1 0.41%
Learning and Instruction 3.73 3 620 16 8 1.29%
Minerva 1.00 68 1,682 0 0 0%
Oxford Review of Education 0.92 75 1,270 5 1 0.08%
Physical Review Special Topics-Physics Education Research 1.34 50 205 3 2 0.98%
Quest 0.69 90 830 1 0 0%
Reading and Writing 1.95 25 669 9 7 1.05%
Reading Research Quarterly 2.57 13 1,231 5 4 0.32%
Reading Teacher 0.88 78 11,006 1 1 0.01%
Research in Higher Education 1.83 30 1,324 8 7 0.53%
Research in Science Education 1.69 36 360 1 0 0%
Research in the Teaching of English 0.85 83 754 4 0 0%
Review of Educational Research 5.46 1 2,605 2 0 0%
Review of Higher Education 2.07 21 1,033 0 0 0%
Review of Research in Education 2.58 12 186 0 0 0%
School Effectiveness and School Improvement 1.16 60 424 4 3 0.71%
Science Education 2.32 17 1,551 6 1 0.06%
Scientific Studies of Reading 3.58 4 205 6 5 2.44%
Second Language Research 1.93 26 195 1 1 0.51%
Sociology of Education 2.73 10 1,639 12 7 0.43%
Sport Education and Society 1.10 62 372 0 0 0%
Studies in Higher Education 1.75 34 1,848 4 2 0.11%
Teachers College Record 1.19 54 7,846 3 0 0%
Teaching and Teacher Education 1.68 37 1,956 3 2 0.10%
Teaching in Higher Education 0.93 72 504 0 0 0%
Teaching of Psychology 0.55 98 2,827 18 7 0.25%
Tesol Quarterly 1.35 49 2,605 6 2 0.08%
Theory Into Practice 0.72 88 728 1 0 0%
Urban Education 1.01 67 1,525 0 0 0%
Vocations and Learning 1.51 41 82 0 0 0%
Zeitschrift fur Erziehungswissenschaft 0.85 81 576 1 0 0%
Note. Journal 5-year impact factors (based on citations in 2011 of articles published 2006 to 2010) and rankings are based on the 2011 Thomson Reuters ISI Web of
Knowledge. Replication rate is the number of replications conducted divided by the total number of articles for that journal.
Table 1 (continued)
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Given such low replication rates, the need to increase is appar-
ent and permeates all levels of education research. We believe that
any finding should be directly replicated before being put in the
WWC. We cannot know with sufficient confidence that an inter-
vention works or that an effect exists until it has been directly
replicated, preferably by independent researchers.
Thankfully, there is an abundance of proposed solutions to
the dearth of replications. To help establish such confidence, the
education sciences could emulate the plan recently announced in
the journal Perspectives on Psychological Science that emphasizes
the importance of “robust, replicable, and generalizable” research.
This plan proposes a new article type, registered replication
reports, which will “consist of multi-lab, high-quality replications
of important psychology experiments along with comments by
the authors of the original studies” (http://www.psychological The replication protocol is
registered ahead of time, and independent labs all working from
this protocol will be part of the eventual publication, with results
reported in aggregate as well as by lab (see also Simons &
Holcombe, 2014). Projects such as this would help bolster both
credibility and understanding of published research. Although
not part of this plan, one such “many-labs replication” has already
been conducted (R. Klein et al., 2013), successfully replicating 10
of 13 attempted psychology studies.
Such initiatives will also help address the rampant problem of
underpowered studies in the social sciences that allow large, but
imprecise, effects sizes to be reported (e.g., Ioannidis, 2012;
Johnson, 2013; McBee & Matthews, 2014a; Pashler & Harris,
2012; Schimmack, 2012). By fostering a system of preregistered
replications, the focus of study can move away from achieving
statistical significance and toward advancing precision in results
(and, more to the point, advancing the education sciences). This
is particularly true if replications focus on studies whose results
may substantially change the outlook of the field, draw height-
ened attention (citations or media coverage), and/or have major
policy implications.
Table 2
Replication Results in Education Journals
Replication Outcome
Replication Type Success Failure Mixed Total
Direct 45 (71.4%) 11 (17.5%) 7 (11.1%) 63 (28.5%)
Conceptual 101 (66%) 18 (11.8%) 34 (22.2%) 153 (69.2%)
Mixed 3 (60%) 0 (0%) 2 (40%) 5 (2.3%)
Total 149 (67.4%) 29 (13.1%) 43 (19.5%) 221 (100%)
Replication Authorship Success Failure Mixed Total
Same Publication as Original 47 (88.7%) 2 (3.8%) 4 (7.5%9) 53 (24%)
Same Authors New Publication 48 (70.6%) 6 (8.8%) 14 (20.6%) 68 (30.8%)
All Unique Authors 54 (54%) 21 (21%) 25 (25%) 100 (45.2%)
Note. A total of 164,589 articles were analyzed using the complete publication history of the 100 journals with highest 5-year impact factors in 2011. Of the 461 that used
the term replicat* in text, 221 were deemed replications.
1900s 1910s1920s1930s 1940s 1950s 1960s 1970s1980s 1990s2000s2010s
Number of Publicaons
Total Arcles Published Use of "replicat*" Replicaon Rate
FIGURE 1. Replication rate in education journals
The solid line depicts the percentage of articles using replicat*. The dotted line depicts the actual replication rate. Each bar represents the
number of articles published in each decade. The 2010s data are based on data from only 2010 to 2012.
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Although universal standards of conducting replications have
(obviously) not yet been adopted, some have been proposed
(e.g., Brandt et al., 2014; Open Science Collaboration, 2012).
Others have also rightly noted that training in conducting repli-
cations is also needed and can be implemented in graduate and
undergraduate student projects (e.g., Frank & Saxe, 2012; Grahe
et al., 2012). Paradoxically, effective training cannot begin until
best practices are established. Similarly, no formalized method
currently exists for how to compare findings of the original and
replicating articles (i.e., no chi-square analysis or Bayesian priors
are required or even typically used).7 The norm is merely to
report whether p values indicate whether direction of results is
the same across studies. The newly proposed aggregating meth-
ods of registered replication reports of reporting independent
and aggregated effect sizes will help compare original and repli-
cated results as well as better estimate true effect sizes. Such
methods slightly shift the emphasis away from the assessment of
whether or not the replication succeeded in replicating the previ-
ous findings and toward winnowing the deep truth about the
magnitude of effects. Similarly, determining what areas merit the
resources needed to provide precise answers is an important
question that currently has no definitive answer. Should it be left
to individual researchers? Should editors request replications of
specific studies? Should the major funding agencies devote
resources specifically to the conduct of replications? The answer
may be a combination of all of the above. But until the biases
discussed in the introduction are removed, the point is moot;
replication will not be conducted or published.
Other scholars have gone so far as to propose that findings be
replicated prior to publication (e.g., Loevinger, 1968; Lubin,
1957; Neuliep & Crandall, 1993b; Roediger, 2012). This rec-
ommendation makes some sense, but it may not be practical—
or possible—in many contexts (Lykken, 1968). Others have
suggested the creation of a journal dedicated specifically to pub-
lishing replication studies (Ahlgren, 1969; Simons, 2012;
Williams, 2012).
Specifically, Neuliep and Crandall (1993b) suggested that
journals reserve a portion of their page space for replication
research (see also Hubbard & Vetter, 1996), which has been
implemented in other domains. For example, the American
Journal of Political Science devoted a special section to publishing
replications in 1996 and 1997 (Meier, 1995a). This policy
resulted in a sharp increase in the number of published replica-
tions, an increase that quickly dissipated when the policy was
discontinued. If page space is a concern, replication publications
could be as short as a paragraph in length (Schlosberg, 1951).
This could be particularly applicable for direct replications.
Indeed, examples of such short-report replications exist but are
rare (e.g., E. Klein, Gould, & Corey, 1969; Tarnowski, Drabman,
Anderson, & Kelly, 1992).
Revising journal editorial policies to provide explicit encourage-
ment for submitting replication studies—and reinforcing the
importance of such studies with reviewers—would help ensure that
the positive trend toward replications found in the current study
continues. A few editors have already begun to do so (e.g., Eich,
2014; McBee & Matthews, 2014b). The increase in open, online
journals may further encourage the submission of replication stud-
ies, as page limits essentially become moot in that context. Explicitly
encouraging the submission of replications may also result in
authors framing their submitted studies as replications.
All this being said, one replication, successful or failed, should
neither cement nor condemn the original finding. The more
replications (and the sooner they are conducted), the better.
Replications will help uncover the precision with which we know
size of the effects, not to mention the extent to which they general-
ize across contexts. As a field, we need to weed out false and nar-
row findings and buttress findings that generalize across contexts.
In confirmatory research, preregistration of predictions, sample
size, power needs, and so on could help avoid questionable
research practices, such as data peeking and data hacking. We need
to foster an environment in which being wrong is not a death
knell and “massaging” data to avoid being wrong is. Research is a
means to an end, and facts are more important to novelty.
Related Recent Initiatives
Many organizations and journals have recently announced
changes relevant to the replication conversation (for longer over-
views of such initiatives, see Makel, 2014; Makel & Plucker,
2014). For example, starting in January 2014, the journal
Psychological Science requires all submissions to disclose facts
about all data points excluded from analyses; all manipulations
reported and not reported; all measures used in the study, includ-
ing those not reported; and how sample size was determined
Additionally, the journal is attempting to promote open research
practices, like data sharing, materials sharing, and preregistering
design and analysis plans prior to data collection by adopting a
badge system developed by the Open Science Collaboration
home/). Third, the journal is seeking to help researchers shift
from a focus on p values toward a focus on “new statistics”
(Cumming, 2014), such as effect sizes and confidence intervals.
Similarly, the PLoS journals require all authors to post all relevant
data and metadata publically (
for-the-open-access-literature-ploss-data-policy/). Open data and
methods are a related and quite relevant topic in that they help
the research community understand how original results were
obtained as well as helping replicators design and conduct repli-
cation research while also providing a barrier to questionable
research practices (e.g., John et al., 2012; Simmons et al., 2011).
A more systematic attempt at direct replication already
underway is the Reproducibility Project (for a review, see
Carpenter, 2012). By attempting to directly replicate findings
from all the 2008 issues of Journal of Personality and Social
Psychology; Psychological Science; and Journal of Experimental
Psychology: Learning, Memory, and Cognition, this group is seek-
ing to uncover common obstacles in conducting replications and
predictors of replication success. To accomplish this, the project
has teams of researchers following a set protocol to conduct rep-
lications with sufficient power.
Not all have been in full support of increased replication
work. Although writing cautions against putting too much
emphasis on and trust in replication attempts, some (e.g., Bissell,
2013; Cesario, 2014) have proposed that published findings
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should be treated with deference until shown otherwise and that
false findings will eventually be discovered and discarded. This
seems like a catch-22; without replication attempts, such weed-
ing out of false findings will be dramatically prolonged.
If a finding does not replicate due to particular circumstances
(e.g., the quality of the teacher or students, the demographics of
the sample, or the classroom climate), then that substantially
weakens the original finding, not the replication. If an effect is so
narrow/fragile that it can be found only in certain environments
under certain circumstances (or by certain researchers), such
limitations either need to be articulated in the original study
(e.g., the effects reported in this study are limited to only envi-
ronments in which the original authors are collecting the data)
or need to be uncovered to avoid overgeneralizing the implica-
tions of the original research. If such boundary conditions are
not reported by the original authors, replication attempts are
needed to identify them.
Finally, we cannot ignore the rash of research fraud allegations
in the social sciences in recent years, many of which allegedly
went undetected for extended periods of time and a few of which
have been confirmed beyond a reasonable doubt. The aftermath
of just these few cases has been (a) policymaker and public dis-
trust of research in certain fields, (b) seriously stained careers for
the former students and colleagues of the perpetrators, and (c)
legal, ethical, and reputational headaches (to put it mildly) for the
institutions where the fraud occurred. The education sciences
have been largely free of such scandals in recent years, but the
odds are long that not a single such case exists within the field.
Although retraction of questionable studies is often used to
address this problem, retractions of research papers are often
ignored or can take years to take hold (Lewandowsky, Ecker,
Seifert, Schwarz, & Cook, 2012). In the absence of a culture of
replication, obvious deterrents are lessened, and any fraud will
likely be discovered only after an extended period of time.
A case in point is the 1998 study purporting a link between
common childhood vaccinations and development of autism.8
The impact of the study was tremendous, causing significant pub-
lic debate about the safety of vaccines, with many sources crediting
the study for a still-growing antivaccination movement that has
been indirectly linked to a resurgence of potentially deadly—and
once nearly eradicated—diseases. Studies refuting the vaccine-
autism link, yet using very different methodologies, were pub-
lished almost immediately (e.g., Taylor et al., 1999), but the
best-known direct replication, which also failed, was published
only a full decade later (Hornig et al., 2008). After a series of inves-
tigations found evidence of widespread misconduct in the original
study, the journal retracted it (Editors of The Lancet, 2010).
Science may be self-correcting, but the often glacial pace of that
correction does not match the speed of dissemination when results
enter the public consciousness. Would some of the remedies sug-
gested above, such as requiring replication of the study before
publication, have prevented this situation? Perhaps, or perhaps
not, but given the tremendous negative impact of the study, it is
difficult to argue that the situation could have possibly been worse.
The Lancets retraction case may be an outlier, but the number of
such outliers is rapidly on the rise. In the first decade of the 21st
century, the number of academic articles within Web of Science
increased 44%, but the number of retractions increased over
1,300% from 30 per year to over 400 (Van Noorden, 2011). The
growth in retractions illustrates the need for making use of the arse-
nal of tools at our disposal for deciphering fact from fiction. Directly
replicating the results of others is a vital part of that process.
Like Campbell and Stanley (1963) noted a half century ago about
experimental design, replication is not a panacea (Makel et al.,
2012; Pashler & Wagenmakers, 2012). It will not resolve all issues
and concerns about rigor, reliability, precision, and validity of edu-
cation research. However, implicitly or explicitly dismissing repli-
cation indicates a value of novelty over truth (Nosek, Spies, &
Motyl, 2012) and a serious misunderstanding of both science and
creativity. If education research is to be relied upon to develop
sound policy and practice, then conducting replications on impor-
tant findings is essential to moving toward a more reliable and
trustworthy understanding of educational environments. Although
potentially beneficial for the individual researcher, an overreliance
on large effects from single studies drastically weakens the field as
well as the likelihood of effective, evidence-based policy. By help-
ing, as Carl Sagan (1997) noted, winnow deep truths from deep
nonsense, direct replication of important educational findings will
lead to stronger policy recommendations while also making such
recommendations more likely to improve education practice and,
ultimately, the lives of children.
2Crocker and Cooper (2011) also point out that an academic
culture that reviles replication makes uncovering fraudulent research
extremely difficult and extends the length of time fraudulent (and all
false) findings stand unquestioned.
3Although it should be noted that the view of too few replications
is not universal (cf. Bissell, 2013; Ostrom, 1971).
4The American Educational Research Association (AERA) explic-
itly addresses the importance of replication and the responsibility of
education researchers to present and preserve their work in a form to
enable replication. In 2006, AERA was the first among research societ-
ies in the social and behavioral sciences to issue Standards for Reporting
on Empirical Social Science Research in AERA Publications that note
the importance of replication in Standards 3.2, 5.6, and 7.5. The AERA
Code of Ethics adopted in 2011 also speaks to standards for reporting
on research and data sharing to allow for replication. AERA is currently
turning its attention to ways that AERA and the journal publications
program can further support and encourage replication studies. The
decision by AERA in 2013 to publish AERA Open as an open-access
journal is one vehicle to encourage the publication of peer-reviewed
replication studies (Felice Levine, AERA Executive Director, personal
communication, December 2013).
5The 2011 JCR Social Science Edition of ISI Web of Knowledge
does not have a calculated entry for 5-year impact factor for Educational
Researcher (ER), and so it is not included in the current results.
Regardless, the replicat* search term indicates 0 results for ER, so inclu-
sion of the journal would not appreciably change the reported results.
6Only articles that replicated previously published findings were
included in this comparison; articles that replicated only another study
from the same publication were not included.
7The authors appreciate an anonymous reviewer who noted this
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8At the time of this writing, the original, redacted paper has been
cited over 1,700 times. We would prefer not to add to its negative
impact by citing it again, although the study can be found by accessing
the cited retraction.
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MATTHEW C. MAKEL, PhD, is a gifted education research specialist
at the Duke University Talent Identification Program, 300 Fuller
Street, Durham, NC 27701; His research
focuses on research methods and academic talent development.
JONATHAN A. PLUCKER, PhD, is the Raymond Neag Endowed
Professor of Educational Leadership and professor of educational psy-
chology in the Neag School of Education at the University of
Connecticut, 2131 Hillside Road, Storrs, CT 06269; jonathan.plucker@ His research focuses on education policy, talent develop-
ment, and program evaluation.
Manuscript received July 31, 2013
Revisions received October 14, 2013,
April 10, 2014, and June 4, 2014
Accepted June 26, 2014
by guest on August 17, 2014http://er.aera.netDownloaded from
... Given the importance of replication in the educational sciences to build generalizable evidence (Makel & Plucker, 2014), this study builds on the findings of Sandilos et al. (2020) in several ways. First, it examines the associations between SEL implementation, teacher-student interactions, and teacher well-being within a different developmental context and with an intervention program (SSIS SEL CIP) for which there is no published literature examining these relations. ...
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... The purpose of this research is to address the need for additional studies related to the prevention of at-risk students not graduating from suburban high schools (Balfanz & Legters, 2004;Bowers, 2010) while addressing the lack of replication studies in the field of education Utilizing the Bowers and Sprott Indicator 3 (Makel & Plucker, 2014) and capitalizing on student-level data in a single school setting. To satisfy these purposes, we asked the following research questions: ...
... Across the SLR and responses to our survey, we observed the challenges of connecting CSEd researchers to literature and datasets, to one another for collaboration, to mentors for professional development, to technical training on data analysis and instrument validation, to gaps in knowledge and practitioner needs, and to funding. Many of these challenges lead to practices that plague the integrity of education research: over-collecting and selectively reporting data, consciously or otherwise, until the hypothesis and/or previous findings are supported; direct use and meta-analysis of randomized-controlled experiments and quasi-experimental design; and post-hoc hypothesizing, instrumentation design, and evaluation after the intervention was delivered or data were collected [15,36,41,52]. However, overwhelmingly, the greatest challenge described by participants and the literature is a lack of time. ...
... Then, the independent and dependent variables of these two cross-sectional data sets were compared in order to capture fluctuations and performance trends over time and introduced some additional control variables. This article underlines the importance of replication in educational research (Makel and Plucker, 2014) as it contributes with a replication of the relationships found in Boman (2022b) by utilizing similar data sources, analytical strategies, dependent, and independent variables. It does also contribute to scholarly discussions on using aggregated data in relation to educational research. ...
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The current study analyzed the relationships between explanatory variables such as socioeconomic status (SES), migration background (MB), and formal teacher competence, and aggregated grades in the Swedish lower-secondary school context by using aggregated municipality data from 2013, 2018, and 2019. SES indicators had larger effect sizes when data from different years were merged and when the outcome variable was changed to an alternative measure of educational achievement. In one model, the MB variable even became statistically insignificant. These results indicate that SES is an important variable which explains a substantial amount of variance in regard to school achievement indicators such as grade point average. Nonetheless, aggregated data may still suffer from omitted variable bias and biased effect size estimates.
... There are also some recommendations for further research on blog-mediated L2 writing instruction. First, with an increasing emphasis on replication studies in education (Makel & Plucker, 2014) and L2 writing research (Porte, & Richards, 2012), it is recommended that the present study be replicated with different types of EFL learners from different educational contexts with various types of blogging technologies. Second, the present study treated L2 writing anxiety as a unitary concept; however, in the review of the related literature, it was pointed out that L2 writing anxiety is multidimensional in nature (Cheng, 2004;Rankin-Brown, 2006). ...
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This study investigated the effects of blog-mediated instruction on learners' writing performance and anxiety as learners of English as a foreign language (EFL). In addition, it aimed to probe into the EFL learners' attitudes towards blog-mediated writing instruction. The participants of the study included 46 Iranian EFL learners from two intact university classes, who were randomly assigned to the control group (N = 21) and the experimental group (N = 25). Over a 16-week university semester, the control group was taught using traditional writing instruction while the experimental group was taught using a blog-mediated writing course. The data were collected through two timed writing tasks, Second Language Writing Anxiety Inventory (Cheng, 2004), and semi-structured interviews. The results indicated that although both groups benefited from their writing sessions, there was a significant difference in the positive effects of blog-mediated and traditional writing instruction on L2 writing performance, showing that the experimental group had a better performance on the posttest writing performance task than the control group. The results also revealed that the blog-mediated course reduced the participants' L2 writing anxiety in the experimental group while traditional instruction did not have positive effects on reducing L2 writing anxiety in the control group. The data from semi-structures interviews indicated that the interviewees from the experimental group were generally positive about the blog-mediated writing course, with little skepticism and negativism echoed about the course. The findings offer significant implications for theory and practice on L2 writing instruction.
... Since Moodle is a very new technology in L2 research and is still very rarely adopted by practitioners, further empirical studies are required to be carried out to illuminate the utility of this CMS platform in enhancing L2 abilities for different learners in various contexts. Moreover, because of contextual variables and other intervening factors, the need for conduction of replication studies in education is acknowledged and warranted (Makel & Plucker, 2014). Hence, to shed more light on the utility of Moodle as a popular CMS platform, the purpose of the current study was set to examine the impact of a Moodle-supported blended language course on the grammar performance of Iranian EFL learners. ...
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Although the use of course management systems has increased significantly in educational settings, these new user-friendly systems have not been widely employed by second language (L2) institutions and practitioners. As an attempt to shed more light on the effectiveness of Moodle as one of the most popular course management systems for educational purposes, the purpose of the current study was to examine the impact of a Moodle-supported language course on the grammar performance of Iranian English as a Foreign Language (EFL) learners. To accomplish this objective, a number of 46 Iranian EFL learners who were the students of two intact classes took part in the research as the participants. One of the classes (n=25) was randomly assigned to the experimental group, and the other class (n=21) was considered as the control group of the experiment. The students of the experimental group were instructed in a blended learning mode in which Moodle was supplemented to face-to-face, traditional teaching, while the students of the control group were instructed traditionally without the aid of Moodle. To measure the dependent variable of the study, a grammar test constructed by the researchers was given to the students as the pre-test and post-test of the experiment. The results of data analysis indicated that the Moodle group students outperformed the traditional group students in terms of grammar performance, suggesting that CMS-supported grammar instruction via Moodle contributed to improving the EFL students' grammar performance. The discussion and the implications of the study were finally presented.
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What causes mobile gamers to be unwilling to spend money on in-app purchases (IAP)? This paper answers this question in two studies. The first study develops and validates the new construct of perceived aggressive monetization (PAM). The second study tests the proposed model with a total sample of 527 US and 526 Australian mobile gamers. The result shows a separate decision mechanism between conversion (i.e., to spend money or not) and the spending size (i.e., how much money to spend). PAM increases users' likelihood of spending nothing on IAP, while perceived fairness decreases that likelihood. However, neither influences how much money the users spend once they decide to spend money. The users' Willingness to Spend and the Time-spent playing the game explained both the conversion and the spending size. There is also a significant interaction between willingness to spend and self-control in explaining the size of spending.
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Over the past two decades, educational policymakers in many countries have favored evidence-based educational programs and interventions. However, evidence-based education (EBE) has met with growing resistance from educational researchers. This article analyzes the objections against EBE and its preference for randomized controlled trials (RCTs). We conclude that the objections call for adjustments but do not justify abandoning EBE. Three future directions could make education more evidence-based whilst taking the objections against EBE into account: (1) study local factors, mechanisms, and implementation fidelity in RCTs, (2) utilize and improve the available longitudinal performance data, and (3) use integrated interventions and outcome measures.
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The data includes measures collected for the two experiments reported in “False-Positive Psychology” [1] where listening to a randomly assigned song made people feel younger (Study 1) or actually be younger (Study 2). These data are useful because they illustrate inflations of false positive rates due to flexibility in data collection, analysis, and reporting of results. Data are useful for educational purposes.