PONE-D-24-01231Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experimentsPLOS ONE

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Additional Editor Comments:

Dear authors,

Your paper was sent to multiple potential reviewers. Unfortunately, most of them declined the request to review this manuscript. Nevertheless, I have now received feedback from one reviewer with considerable knowledge and expertise in meta-analytic work and various theories from economics. The reviewer notes several positive aspects of your work. However, he/she also expresses certain concerns. The comments from the reviewer are attached below.

Based on the input from the reviewer and my own reading of the manuscript, I am willing to provide you with the opportunity to move this manuscript into a second round of reviews. Thus, I invite you to undertake a revision with a relatively clear path toward publication as long as you meticulously address all the feedback points from the reviewer and incorporate needed changes or edits to the manuscript.

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Kind regards,

Tobias Otterbring

Handing Editor, PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper reports the results of a study investigating how callback rates from correspondence studies investigating labor market discrimination might be predicted by the social signals of warmth and competence. The authors do so by meta-analyzing 21 published correspondence studies and collecting ratings of warmth and competence from a set of independent raters (Prolific participants). The authors find that social perceptions of warmth and competence predicts callback disparities for studies varying race and gender, but that the results are inconsistent for studies varying other categories such as sexuality or disability.

I would like to applaud the authors for their extensive and transparent reporting of their methods, which makes all analytical decisions and the corresponding results, concise and clear in their manuscript. In relation to this, it’s also great to see that the authors test the sensitivity of their analytical strategies by e.g. calculating various forms heterogeneity measures (instead of just relying on one type of analysis) to gain a fuller picture of the between- and within-study heterogeneity in the analyzed studies.

In general, I think the authors have done a very good job in clearly presenting their methods, results and a clear discussion of the theoretical and practical implications of these findings. That is, I think the paper is already in very good shape. However, throughout my review of the manuscript, I identified some issues that I outline below.

1. From the methods section, it is unclear what the authors’ justification is for using a random-effects model meta-analysis. I think it would be valuable to include a short (1-3 sentences) argument for why this model is the appropriate one to use. For instance, did all papers only include one study or did some include multiple? Did several papers originate from the same set of authors/universities/labs? Basically, are there any specific clusters in the data that would have made a mixed-effect model meta-analysis a more appropriate choice?

2. What is the rationale for the sample size of the Prolific raters? Also, was this a convenience (vs. nationally representative) Prolific sample and how was the sample quality-checked? Were there for instance any attention/bot/comprehension/quality- checks? Since the ICC’s are also an integral part of assessing the quality of the rating for the raters, I suggest the authors draw these results more to the front in the method section on p. 5.

3. I was also wondering what the rationale for using a Prolific sample is here? Would the authors have expected a different result if e.g. the raters had previous experience with hiring decisions?

4. On page 5 the authors report “…a marginally significant difference in competence between black and white (-11.52, p = .06)”. Considering the massive problems of replication and reproducibility across the behavioral science, I suggest the authors refrain from using such language.

5. On p. 6, the authors highlight the large prediction interval for the meta-analysis because of the substantial heterogeneity. I think the authors could address this result more in the discussion; for instance, could this result allude to that this literature could benefit from a more standardized approach of testing for labor market discrimination?

6. I was surprised to see that the authors do not test for publication bias in the meta-analysis. If substantial publication bias is present in the current literature, this will naturally influence how much we can trust the findings of the meta-analysis. I think the authors need to conduct analysis of publication bias and correct the estimates for publication-bias.

7. The exploratory analysis in the last paragraph of section 3 (page 8) only reports the correlation coefficients but not p-values and CI’s. I would encourage the authors to report these test statistics to allow the reader a more complete overview of these results.

8. Some signals, such as those for wealth and sexuality, yield vastly different ICC’s as noted by the authors in the first paragraph of section 4 (page. 8). Why might this be? Could it be a result of certain individual differences in e.g. political orientation etc.? I think it would be valuable if the authors further explore this to identify if there are any systematic influences for these differences. The authors outline this as point of discussion on page 10., but I think they could have elaborated more on this aspect.

9. Lastly, while I agree that the present work can be valuable for the development of responsible AI, I think the authors need to expand on this argument. Right now, this implication comes of as overstated and general.

Having mentioned these issues, I wish the authors all the best for their project and I am truly looking forward to reading their revised manuscript, either again for review or published.

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Reviewer #1: Yes: Christian T. Elbaek

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Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments

PONE-D-24-01231R1

Dear Dr. Hausladen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Tobias Otterbring

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear authors,

Based on the assessment of the expert reviewer and my own assessment of your manuscript, I am happy to inform you that my recommendation as the handling editor is to accept your paper for publication in PLOS One in its current form. Well done!

Kind regards,

Tobias Otterbring

Handling Editor

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Christian T. Elbaek

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