Publication Bias: Causes, Detection, and Remediation

Publication Bias: Causes, Detection, and Remediation

1 VCU School of Business, Department of Management Publication Bias: Causes, Detection, and Remediation Sven Kepes and Michael A. McDaniel Virginia Commonwealth University AOM PDW August 9, 2013 Orlando, FL 2 VCU School of Business, Department of Management Overview

Introduce publication bias analyses as one form sensitivity analysis in metaanalysis. Briefly review a few non-publication bias approaches to sensitivity analysis. Focus on publication bias as a sensitivity analysis: Causes of publication bias Overview of methods for detection and assessment 3 VCU School of Business, Department of Management Sensitivity Analysis 4

VCU School of Business, Department of Management Sensitivity Analysis A sensitivity analysis examines the extent to which results and conclusions are altered as a result of changes in the data or analysis approach (Greenhouse & Iyengar, 2009). If the conclusions do not change as a result of the sensitivity analysis, one can state that the conclusions are robust and one can have greater confidence in the conclusions. 5

VCU School of Business, Department of Management Sensitivity Analysis Sensitivity analyses are rarely conducted in meta-analyses in the organizational sciences (Kepes, McDaniel, Brannick, & Banks, 2013). Because meta-analyses have a strong impact on literatures, sensitivity analyses need to become much more common (and reported) in metaanalyses. 6 VCU School of Business, Department of Management Sensitivity Analysis:

Outliers One form of sensitivity analysis is to conduct meta-analyses with and without outliers. Only 3% of meta-analyses conduct outlier analyses (Aguinis et al., 2011). Effect size outlier (large or small) Graphical methods and statistical tests for outliers (e.g., SAMD statistic; Beal, Corey, & Dunlap, 2002) Sample size outlier (large) Sample sizes influence effect size weights in metaanalyses. 7 VCU School of Business, Department of Management

Sensitivity Analysis: Outliers One sample removed analysis: Repeat the meta-analysis multiple times, each time leaving out one sample. This yields as many means as samples. Examine the means. How much does the distribution mean change when a given sample is excluded from the analysis? Are the results due to a small number of influential samples? 8 VCU School of Business, Department of Management

Sensitivity Analysis: Operational definitions Measures of a given construct often vary within a literature area/meta-analysis. Beauty might be measured by: Self-reports, observations of others, facial or body symmetry, etc. The magnitude of effects may co-vary with the operational definitions of variables. Are the results due to a specific operational definition/measure? 9 VCU School of Business, Department of Management

Sensitivity Analysis: Data imputations Typically, one does not include a sample in a meta-analysis if the sample size and effect size are not known with certainty. However, meta-analyses that involve corrections for artifacts (i.e., measurement error or range restriction) often need to impute at least some of the artifacts for some of the samples. 10 VCU School of Business, Department of Management Sensitivity Analysis:

Data imputations Consider various imputed values. After you identify what you believe are the best imputations, create sets of artifacts that have higher values, sets with lower values, and sets with more or less variance. How robust are the conclusions to varying assumptions about the mean and variability of the imputed artifacts? 11 VCU School of Business, Department of Management Sensitivity Analysis: Publication bias

Publication bias analyses are a type of sensitivity analysis. Publication bias exists when the research available to the reviewer on a topic is unrepresentative of all the literature on the topic (Rothstein et al., 2005). 12 VCU School of Business, Department of Management Sensitivity Analysis: Publication bias Only between 3% (Aguinis et al., 2011) and 30% (Kepes et al., 2012) of metaanalyses conduct publication bias analyses (typically with inappropriate

methods; Banks et al., 2012; Kepes et al., 2012). Similar terms/phenomena: Availability bias, dissemination bias 13 VCU School of Business, Department of Management Sensitivity Analysis: Publication bias Publication bias can distort a literature. A meta-analysis of a literature distorted by publication bias will yield incorrect results. Taxonomy of causes of publication bias (Banks & McDaniel, 2011; Kepes et al.

2012) Outcome-level causes Sample-level causes 14 VCU School of Business, Department of Management Outcome-level Publication Bias in Primary Studies Outcome-level publication bias refers to selective reporting of results (i.e., selective reporting of effect sizes). In other words, the primary study is available but some results are not reported. 15

VCU School of Business, Department of Management Publication Bias: Outcome-level There is substantial evidence of this bias in the medical science literatures. There is no compelling argument for a different situation in the organizational sciences (Hopewell, Clarke, & Mallett, 2005). 16 VCU School of Business, Department of Management Publication Bias:

Outcome-level Sources of this bias include author decisions, the editorial review process, and organizational constraints. 17 VCU School of Business, Department of Management Publication Bias: Outcome-level Authors may decide to exclude some effect sizes prior to submitting the paper. Not statistically significant Contrary to:

expected finding the authors theoretical position the editors or reviewers theoretical positions past research Results that disrupt the papers story line. Manufacture false results (Yong, 2012). 18 VCU School of Business, Department of Management Publication Bias: Outcome-level

Authors may also: Choose the analytic method that maximizes the magnitude of the effect size. Not report the effect size under alternative analysis methods. Delete the effect sizes that are not consistent with expected results. 19 VCU School of Business, Department of Management Publication Bias: Outcome-level Authors may engage in HARKing (hypothesizing after results are known)

(Kerr, 1998). HARKing may involve deleting some effect sizes. HARKing serves to convert Type I errors into non-replicable theory, and hides null results from future generations of researchers (Rupp, 2011, p. 486). 20 VCU School of Business, Department of Management Publication Bias: Outcome-level Bedeian, Taylor, and Miller (2010) reported that 92% of faculty know of a colleague who has engaged in HARKing.

This a sad state of affairs. 21 VCU School of Business, Department of Management Publication Bias: Outcome-level For disciplines that use many control variables, a researcher can go fishing for the control variables that yield the expected results. Discard the control variables that yield results inconsistent with the expected result. Fail to report the effect sizes prior to fishing. 22

VCU School of Business, Department of Management Publication Bias: Outcome-level The editorial review process can result in outcome-level bias. Reviewers and editors may promote HARKing by knowing the results and then offering alternative explanations. 23 VCU School of Business, Department of Management Publication Bias: Outcome-level

An editor or reviewer may: Request that the author change the focus of the paper, making some results less relevant. Request that the author shorten the paper. Request that the author drop the analyses yielding statistically non-significant effect sizes. 24 VCU School of Business, Department of Management Sample-level Publication Bias in Primary Studies Sample-level causes of publication bias concern the non-availability of an entire sample.

25 VCU School of Business, Department of Management Publication Bias: Sample-level Sources of this bias include author decisions, the editorial review process, and organizational constraints. 26 VCU School of Business, Department of Management Publication Bias: Sample-level Research in medicine suggests that

author decisions are the primary cause of non-publication and thus missing samples (Dickerson, 1990, 2005). An author will likely work on the paper that has the best chance of getting into the best journal. Other papers are abandoned. Results in small magnitude effects being hidden from the publically available research literature. 27 VCU School of Business, Department of Management Publication Bias: Sample-level Authors may have personal norms or adopt organizational norms that hold that

only articles in top journals count. Count for tenure, promotions, raises, discretionary dollars. Thus, authors may abandon papers that dont make the top journal cut. Results are lost to the literature. 28 VCU School of Business, Department of Management Publication Bias: Sample-level The editorial process will reject: Poorly framed papers Papers without statistically significant findings Papers with results contrary to existing literature

and current theory Well done papers with research that didnt work 29 VCU School of Business, Department of Management Publication Bias: Sample-level These editorial decisions result in suppression of effect sizes at the sample-level. Typically, samples with smaller magnitude effect sizes will be lost. 30

VCU School of Business, Department of Management Publication Bias: Sample-level To clarify, we believe that editors should reject papers that are bad (e.g., bad framing, lack of clear focus, incomplete theory, poorly developed hypotheses, awful measures, poorly designed, inappropriate analysis). Just dont define bad as: Small effect sizes Results inconsistent with hypotheses 31 VCU School of Business, Department of Management

Publication Bias: Sample-level Organizations may not give permission to report some findings. Organizations are unlikely to permit release of a paper if it documents that employment decisions (e.g., selection, layoffs, raises, or bonuses) show mean demographic differences. 32 VCU School of Business, Department of Management Publication Bias: Sample-level Some research is asserted to be

proprietary. Try requesting technical documentation from employment test vendors who claim that their employment test has much smaller mean demographic differences than typically observed. 33 VCU School of Business, Department of Management Publication Bias: Sample-level Neither outcome-level publication bias nor sample-level publication bias results in a missing data at random situation. Not missing at random (NMAR)

There is nothing random about it. 34 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? 35 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? Hypotheses in our journals are almost always supported (e.g., Fanelli, 2010;

Sterling & Rosenbaum, 1995). Negative results are disappearing from our published literature (Fanelli, 2012). Are we approaching omniscience or there are forces at work that cause our journal articles to be unrepresentative of all completed research (Kepes & McDaniel, in press)? 36 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? Dalton, Aguinis, Dalton, Bosco, and

Pierce (2012, p. 222) stated that publication bias does not produce an inflation bias and does not pose a serious threat to the validity of metaanalytically derived conclusions. Vote counting study of the significance and nonsignificance of correlations. Took a broad inferential leap. 37 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? Dalton et al. (2012) noted a potentially important limitation of their study: We have not, however, established this phenomenon at the focal level. Our data do not

provide an insight into whether such comparisons would maintain for studies published and nonpublishedparticularly focused on, for example, the Big Five personality traits or employee withdrawal behaviors (e.g., absenteeism, transfers, and turnover). (p. 244) 38 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? When examining at a focal level (a literature on a specific topic), publication bias appears to be relatively common. Ferguson and Brannick (2012) examined

meta-analyses in the psychological literature. Their conclusions: Publication bias exists in 40% of published metaanalyses Publication bias was worrisome in about 25% of meta-analyses 39 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? Judgment and decision making (Renkewitz, Fuchs, & Fiedler, 2011) Test vendor validity data (McDaniel, Rothstein, Whetzel, 2006; Pollack & McDaniel, 2008)

Conditional Reasoning Test validity (Banks, Kepes, & McDaniel, 2012) Big 5 validity (Kepes, McDaniel, Banks, Hurtz, & Donovan, 2011) 40 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? Reactions to training (Kepes, Banks, McDaniel, & Sitzmann, 2012) Relation between work experience and performance (Kepes, Banks, & Oh, in press) Gender differences on transformational

leadership (Kepes, Banks, & Oh, in press) 41 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? Pygmalion interventions (Kepes, Banks, & Oh, in press) Journal-published mean racial differences in personality (Tate & McDaniel, 2008) Journal-published mean racial differences in job performance (McDaniel, McKay, & Rothstein, 2006)

42 VCU School of Business, Department of Management Is Publication Bias in Our Literature Areas? In the next few years, we will likely see many more studies examining publication bias on topics in strategy, entrepreneurship, and other organizational sciences. 43 VCU School of Business, Department of Management

Is Publication Bias in Our Literature Areas? Publication bias analyses of already completed meta-analyses are relatively easy to do. Data are often listed in tables or at least the studies are listed in the reference section. Software is readily available. Although one might hop from one package to another: R, Stata, Comprehensive Meta-analysis (CMA), etc. 44 VCU School of Business, Department of Management

Methods Kepes, S., Banks, G.C., McDaniel, M.A., & Whetzel, D.L. (2012). Publication bias in the organizational sciences. Organizational Research Methods, 15, 624-662. 45 VCU School of Business, Department of Management Fail Safe N Rosenthal (1979) introduced what he called the file drawer problem. Argument is one of sample-level bias. His concern was that some non-significant studies may be missing from an analysis (i.e., hidden in a file drawer) and that these studies, if

included, would nullify the observed effect. 46 VCU School of Business, Department of Management Fail Safe N Rosenthal suggested that rather than speculate on whether the file drawer problem existed, the actual number of studies that would be required to nullify the effect could be calculated. Cooper (1979) called this number the fail safe sample size or Fail Safe N. 47

VCU School of Business, Department of Management Fail Safe N Becker (2005) argued that Fail Safe N should be abandoned as a publication bias method. Different approaches yield widely varying estimates of the Fail Safe N. Prone to miss-interpretation and misuse. No statistical criteria available to aid interpretation. 48 VCU School of Business, Department of Management Fail Safe N

More from Becker (2005) The assumption of a zero effect for the missing studies is likely to be biased (Begg & Berlin, 1988). The Fail Safe N does not incorporate sample size information (Sutton et al., 2000) 49 VCU School of Business, Department of Management Fail Safe N Conclusion: Authors should stop using the Fail Safe N. Editors and reviewers should stop recommending the use the of the Fail Safe N.

50 VCU School of Business, Department of Management Study Source Comparison A common study source analysis is to compare published vs. unpublished samples. 51 VCU School of Business, Department of Management Study Source Comparison One is implicitly making the assumptions that: The published samples are representative of all

published samples. The unpublished samples are representative of all unpublished samples. These assumptions are not likely credible (Hopewell et al., 2005) 52 VCU School of Business, Department of Management Study Source Comparison Consider unpublished samples. Meta-analyses may oversample from particular sources: Unpublished samples in meta-analyses are often authored by those who are authors of the metaanalysis (Ferguson & Brannick, 2012).

53 VCU School of Business, Department of Management Study Source Comparison Encourage searching for unpublished samples and conduct published vs. unpublished moderator analyses. That practice alone is an insufficient approach to assessing publication bias. 54 VCU School of Business, Department of Management Symmetry-based Methods

When sampling error is the sole source of variance, and the sampling distribution is symmetrical, then a funnel plot can be examined for symmetry. A funnel plot is a plot of effect sizes by precision (1/standard error). 55 VCU School of Business, Department of Management Symmetry-based Methods Funnel Plot of Precision by Fisher's Z 30 Precision (1/Std Err)

20 10 0 -2.0 -1.5 -1.0 -0.5 0.0 Fisher's Z 0.5

1.0 1.5 2.0 56 VCU School of Business, Department of Management Symmetry-based Methods At non-zero population values, the sampling distribution of a correlation is asymmetrical. Transform correlations into Fisher z.

57 VCU School of Business, Department of Management Symmetry-based Methods Source: http://luna.cas.usf.edu/~mbrannic/ files/regr ession/corr1.html 58 VCU School of Business, Department of Management Symmetry-based Methods Asymmetry may be a sign of publication bias.

Asymmetry is typically due to the suppression of statistically non-significant effect sizes from small samples. Small samples with large magnitude effects, likely statistically significant effects, have a higher probability of being published than small samples with nonsignificant small magnitude effects. 59 VCU School of Business, Department of Management Symmetry-based Methods Asymmetrical funnel plot: Funnel Plot of Precision by Fisher's Z 30 Precision (1/Std Err)

20 10 0 -2.0 -1.5 -1.0 -0.5 0.0 Fisher's Z

0.5 1.0 1.5 2.0 60 VCU School of Business, Department of Management Symmetry-based Methods Asymmetry may be a sign of publication bias. Asymmetry may also be due to likely suppressed samples that have larger magnitude effect sizes.

The suppression would not be a function of statistical significance. Larger effects may be suppressed because they are socially uncomfortable. Mean demographic differences 61 VCU School of Business, Department of Management Symmetry-based Methods Asymmetrical funnel plot: Funnel Plot of Precision by Fisher's Z 30 Precision (1/Std Err)

20 10 0 -2.0 -1.5 -1.0 -0.5 0.0 Fisher's Z

0.5 1.0 1.5 2.0 62 VCU School of Business, Department of Management Symmetry-based Methods Sample size (or precision) should not be correlated with effect size. Begg and Mazumdars Rank Correlation Test (Begg & Mazumdar, 1994)

Egger's Test of the Intercept (Egger, Smith, Schneider, & Minder, 1997) Duval and Tweedies Trim and Fill (Duval, 2005) 63 VCU School of Business, Department of Management Symmetry-based Methods Trim and fill Funnel Plot of Precision by Fisher's Z 30 Funnel Plot of Precision by Fisher's Z

10 30 0 -2.0 -1.5 -1.0 -0.5 0.0 Fisher's Z 0.5

1.0 1.5 20 Precision (1/Std Err) Precision (1/Std Err) 20 2.0 10 0

-2.0 -1.5 -1.0 -0.5 0.0 Fisher's Z 0.5 1.0 1.5

2.0 64 VCU School of Business, Department of Management Symmetry-based Methods Symmetry methods are not robust to violations of the assumption of sampling error being the sole source of variance (e.g., moderator variance; Terrin et al., 2003). Our disciplines abound with moderators. Apply the methods to relatively moderator free subsets of the data. At least 10 effect sizes (Sterne et al., 2011)

65 VCU School of Business, Department of Management Symmetry-based Methods The trim and fill method is probably the most useful symmetry based method in that it estimates what the population distribution would be if the missing studies were located. Analyses are re-conducted on the distribution containing both the observed data and the imputed data. 66 VCU School of Business, Department of Management

Symmetry-based Methods It is unwise to consider this distribution of observed and imputed data as the true distribution. 67 VCU School of Business, Department of Management Symmetry-based Methods More reasonable to compare the observed mean with the trim and fill adjusted mean. If the mean drops from .45 to .15, one should worry about publication bias. But, one should not assume that .15 is

the best estimate of the population mean. 68 VCU School of Business, Department of Management Symmetry-based Methods Some asymmetry is not due to publication bias but to small sample effects. A medicine may work best with the sickest (small N) patients and work less well with moderately sick (larger N) patients. Small sample studies may yield larger effects due to better measures that are more difficult to collect in larger samples.

69 VCU School of Business, Department of Management Symmetry-based Methods Related to the funnel plot and trim and fill is the contour-enhanced funnel plot, which displays graphically whether the imputed samples are a function of statistical significance (Peters et al., 2008). Helps separate publication bias effects from small sample effects. 70 VCU School of Business, Department of Management

Symmetry-based Methods Contour-enhanced funnel plot 30 Observed samples p < 5% 5% < p < 10% Inverse standard error p > 10% Filled samples mean fz_obs 20

trim & fill adj. mean fz_obs 10 0 -.4 -.2 0 Effect estimate .2 .4

71 VCU School of Business, Department of Management Symmetry-based Methods Software for symmetry-based analyses: See Borenstein (2005) Comprehensive Meta-analysis (CMA) ( www.meta-analysis.com) R (http://www.r-project.org/) metafor package (www.metafor-project.org) Stata (see http://www.stata.com/meeting / 10uk/meta_stata.pdf) Contour-enhanced funnel plots (the confunnel program; Kepes et al., 2012; Palmer, 2008)

72 VCU School of Business, Department of Management Cumulative Meta-analysis by Precision Sort samples by sample size or precision. Conduct a meta-analysis starting with one effect size (the most precise effect) and add an additional effect size (with increasingly less precision) with each iteration of the meta-analysis. Inspect the meta-analytic means for drift. 73

VCU School of Business, Department of Management Cumulative Meta-analysis by Precision Banks, Kepes, and Banks (2012) showed some drift consistent with an inference of publication bias. 74 VCU School of Business, Department of Management Cumulative Meta-analysis by Precision Ncum Cumulative point estimate (and 95% CI)

The most precise sample (N=542), has an effect size of .06. With 4 studies needed to bring the N to 1,739, the mean effect size is .05. With 10 studies needed to bring the N to over 3,000, the mean effect size is .12. By the time one gets to 48 studies (N = 5,576), the mean effect size is .21. 542 1004 1450

1739 1982 2232 2457 2666 2861 3051 3233 3410 3523 3643 3694 3799 3906 4005 4100 4191

4269 4346 4414 4491 4551 4613 4681 4745 4812 4869 4905 4963 5019 5069 5115 5154 5194

5242 5289 5335 5379 5413 5453 5491 5516 5540 5560 5576 0.060 0.046 0.026 0.046 0.144

0.127 0.143 0.123 0.129 0.118 0.125 0.109 0.130 0.120 0.154 0.170 0.156 0.151 0.137 0.136 0.145 0.150

0.161 0.159 0.172 0.182 0.186 0.194 0.183 0.192 0.206 0.205 0.205 0.211 0.218 0.228 0.237 0.232 0.230

0.226 0.225 0.217 0.214 0.214 0.218 0.214 0.210 0.213 0.213 -0.25 0.00 0.25

0.50 75 VCU School of Business, Department of Management Cumulative Meta-analysis by Precision Gives similar results to that obtained in symmetry based methods. When symmetry analyses suggest small effects are suppressed, cumulative meta-analysis will show a drift toward larger effects. When symmetry analyses suggest larger effects are suppressed, cumulative meta-analysis will show a drift toward smaller effects.

76 VCU School of Business, Department of Management Cumulative Meta-analysis by Precision Possibly less affected by moderator induced heterogeneity. More research is needed. More research is needed on interpretation heuristics for when to judge a drift meaningful. 77 VCU School of Business, Department of Management

Cumulative Meta-analysis by Year of Publication Ioannidis has been very active in demonstrating that effect sizes from the earliest published studies typically overestimate population values (e.g., Ioannidis and Trikalinos, 2005). Proteus phenomenon (from Greek "" - protos, "first")"" - protos, "first") - protos, "" - protos, "first")first"" - protos, "first")) Smaller effect size studies appear to take longer to get published. 78 VCU School of Business, Department of Management

Cumulative Meta-analysis by Year of Publication Year Cumulative point estimate (and 95% CI) Cumulative correlation of conditional reasoning test with job performance by year (Banks, Kepes, & McDaniel, 2012) Earliest studies, on average, show the largest effects. 79

VCU School of Business, Department of Management Cumulative Meta-analysis Software for cumulative meta-analysis Comprehensive Meta-analysis (CMA) ( www.meta-analysis.com) Stata (http:// www.stata.com/bookstore/meta-analysis-in-stata / ; see chapter 1) 80 VCU School of Business, Department of Management Selection Models Selection models, also called weightfunction models, originated in

econometrics to estimate missing data at the item level. Hedges and Vevea introduced the method to the publication bias literature (Hedges, 1992; Vevea & Hedges, 1995). Relatively robust to heterogeneity (Vevea & Woods, 2005). 81 VCU School of Business, Department of Management Selection Models As with trim and fill, selection models estimate what the population distribution would be if the missing studies were located and included in the meta-analytic

distribution. 82 VCU School of Business, Department of Management Selection Models When one is conducting a meta-analysis without regard to suppressed studies, one is implicitly assuming that one has 100% of the completed studies. Selection models permit you to make other assumptions. 83 VCU School of Business, Department of Management

Selection Models Selection models assume that the probability that an effect size is included in a distribution is a function of a characteristic of that effect size. This characteristic is usually the level of statistical significance. Consider an a priori assumed selection model. 84 VCU School of Business, Department of Management Selection Models

An a priori assumed selection model: Significance level Probability of being in the distribution p <.001 100% .001 < p < .049 90% .049 < p < .10 70% p > .10 30%

85 VCU School of Business, Department of Management Selection Models Given an a priori assumed selection model, what would the mean effect be if samples at all statistical significance levels have a 100% probability of inclusion in the distribution? In practice, one may create several a priori selection model and compare the means to the original meta-analytic mean. 86

VCU School of Business, Department of Management Selection Models Software for selection models A priori selection models R code: Field & Gillett, 2010 S-Plus code: Vevea & Woods, 2005 87 VCU School of Business, Department of Management Meta-regression A meta-regression is a regression in which the effect size is the dependent variable and potential moderators are the independent variables.

Egger's Test of the Intercept was noted earlier as a symmetry based method (Egger et al., 1997). 88 VCU School of Business, Department of Management Meta-regression Eggers Test examines whether precision is related to the magnitude of an effect size. Thus, Eggers Test is conceptually similar to a meta-regression with precision as the single independent variable. Effect size = a + b1(precision)

89 VCU School of Business, Department of Management Meta-regression However, other variables (potential moderator variables) could be included: Effect size = a + b1(precision) + b2(moderator) Thus, a single regression might be able to simultaneously evaluate moderators and the presence of publication bias. 90 VCU School of Business, Department of Management

Meta-regression Economists are advocates of this approach. See Doucouliagos and Stanley (2009), Stanley (2008), and Stanley and Doucouliagos (2011). 91 VCU School of Business, Department of Management Meta-regression Software for meta-regression SAS, SPSS, and Stata macros ( http://mason.gmu.edu/~dwilsonb/ma.html) Stata (http:// www.stata.com/bookstore/meta-analysis-in-stata

) Comprehensive Meta-analysis (CMA) ( www.meta-analysis.com) R metafor package (www.metafor-project.org) 92 VCU School of Business, Department of Management Trim and Fill with Meta-regression Begin with a meta-regression where independent variables are moderators. Apply a version of trim and fill to residuals. Impute residuals as needed for symmetry. Compare original meta-regression to trim and fill meta-regression.

See Weinhandl & Duval (2012) 93 VCU School of Business, Department of Management Prevention of Publication Bias 94 VCU School of Business, Department of Management Prevention of Publication Bias Extremely thorough literature review (see Rothstein, 2012)

Published Unpublished Dissertations Conference papers Masters theses Foreign language papers Personal communication with every researcher in the literature 95

VCU School of Business, Department of Management Prevention of Publication Bias Research registries: Database where researchers register the studies that they plan to conduct, are in the process of conducting, or have already conducted (Banks & McDaniel, 2011; Berlin & Ghersi, 2005). Medical sciences (ClinicalTrails.gov) Education (What Works Clearinghouse; U.S. Department of Education) Social work (Campbell Collaboration) Many registries exist in medical research domains. 96

VCU School of Business, Department of Management Prevention of Publication Bias Changes in the journal review process. Many medical journals will not accept studies for review unless the study has been pre-registered. Many medical journals allow for supplemental materials to be offered and made available on the web. Release data (after some time). These journal practices should reduce suppression of effect sizes. 97 VCU School of Business, Department of Management

Prevention of Publication Bias Journals could base acceptance/ rejection decisions on the introduction and the method sections of the paper (2stage review process; see Kepes & McDaniel, in press) Reviewers would not see the results and discussion during the first stage of the review process. Places less reliance on statistical significance as a criterion for acceptance. 98 VCU School of Business, Department of Management Prevention of Publication Bias

Alter author and organizational norms concerning the value of publications in less than elite journals. Stop encouraging the abandonment of research studies when they cannot get into an A journal. Abandonment of research is a very damaging practice for our research literatures. Many of our best universities are promoting the abandonment of research studies. 99 VCU School of Business, Department of Management Prevention of Publication Bias Alter top journals obsession with strong theoretical contributions.

Our discipline has a theory fetish (Hambrick, 2007, p. 1346) what we see too often in our journals: a contorted, misshapen, inelegant product, in which an inherently interesting phenomenon has been subjugated by an ill-fitting theoretical framework (Hambrick, 2007, p. 1349). 100 VCU School of Business, Department of Management Suggested Research Program to Estimate the Extent of Publication Bias 101

VCU School of Business, Department of Management Suggested Research Program Track paper from one point in time to another. Start with dissertations and track the manuscript through the conference and publication cycle to see differences between the results in the dissertation and the results in the final journal article. Which type of results never got accepted at a journal? (hint: those with statistically insignificant findings) Evidence of HARKing. 102

VCU School of Business, Department of Management Suggested Research Program Track paper from one point in time to another. Start with dissertations and track the manuscript through the conference and publication cycle to see differences between the results in the dissertation and the results in the final journal article. Which type of results never got accepted at a journal? (hint: those with statistically insignificant findings) Evidence of HARKing. 103 VCU School of Business, Department of Management

Suggested Research Program Or, start with submission to a conference (e.g., SIOP, AOM) and track the paper through the conference and publication cycle to see differences between the results in the conference submissions and the results in the journal article. 104 VCU School of Business, Department of Management Suggested Research Program 2013 best paper in AOM Research Methods Track: The Chrysalis Effect: How ugly data

metamorphosize into beautiful articles. Ernest H O'Boyle, George Christopher Banks, Erik Gonzalez-Mule Monday, Aug 12 2013 1:15PM - 2:45 pm; Coronado Springs Resort in Yucatan 3 105 VCU School of Business, Department of Management Thank you! 106 VCU School of Business, Department of Management References

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VCU School of Business, Department of Management References Bedeian, A.G., Taylor, S,G. & Miller, A. N. (2010). Management science on the credibility bubble: Cardinal sins and various misdemeanors. Academy of Management Learning & Education, 9, 715725. doi: 10.5465/amle.2010.56659889 Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50, 1088-1101. doi:10.2307/2533446 Begg, C.B. & Berlin, J.A. (1988). Publication bias: A problem in interpreting medical data. Journal of the Royal Statistical Society. Series A (Statistics in Society), 151, 419-463. doi: 10.2307/2982993 Berlin, J.A. & Ghersi, D. (2005). Preventing publication bias: Registries and prospective meta-analysis. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta analysis: Prevention, assessment, and adjustments (pp. 35-48). West Sussex, UK: Wiley. Borenstein, M. (2005). Software for publication bias. In H. R. Rothstein, A. J. Sutton & M.

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presence of heterogeneity. Statistics in Medicine, 22, 2113-2126. doi:10.1002/sim.1461 115 VCU School of Business, Department of Management References Vevea, J. L., & Hedges, L. V. (1995). A general linear model for estimating effect size in the presence of publication bias. Psychometrika, 60, 419-435. doi:10.1007/BF02294384 Vevea, J. L., & Woods , C.M. (2005). Publication bias in research synthesis: Sensitivity analysis using a priori weight functions. Psychological Methods, 10, 428443. Weinhandl, E.D., & Duval, S. (2012). Generalization of trim and fill for application in metaregression. Research Synthesis Methods, 3, 51-67

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