PONE-D-21-35674The PISA data reveal student and school inequality that affects resultsPLOS ONE

Dear Dr. Neuman,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

ACADEMIC EDITOR: Thank you for submitting your work to Plos one. We have now received two reviews. The reviewers are split in their assessment of the paper. One reviewer, with a background in the technical methods used, suggests that the paper nearly meets Plos criteria for publication. The other reviewer, with significant expertise in the subject area, argues that the manuscript, in its current form, is far from meeting Plos criteria for publication. I am recommending a decision of major revisions. To meet Plos criteria for publication, the author must, at minimum, address the concerns of reviewer one related to the measurement of PISA scores (Reviewer 1’s point 2) and better ground the study in the relevant literature and theory. The statistical approach is novel in this field of study; the author just needs to make clear what new knowledge this approach affords over standard statistical approaches used in previous studies or how this study replicates, in a conceptual, previous results. Note that due to the split reviews and the nature of the reviews, I will seek a third party to review any resubmitted manuscript. Thank you once again for submitting your work to Plos One, and I wish the author well in their research endeavors.

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PLOS ONE

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Reviewer #1: No

Reviewer #2: Yes

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

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: I have the following comments on the paper "The PISA data reveal student and school inequality that affect results."

1. The Methods section should be presented before the Results and Discussion sections.

2. The authors calculated students' "normalized points" Pi by dividing points scored per question by the difficulty of each question. The nominator is the difference between the max difficulty and the min difficulty of each student (page 8). First, however, the max and min difficulties of questions answered vary across students, and the gap between the max and min difficulty also varies across students. Therefore, using the nominator as the gap of max and min difficulty of individual students does not normalize PISA scores across the student distribution. Second, PISA test scores have been weighted to take into account the difficulty of different questions and allow meaningful comparisons across students/cohorts/countries. PISA test score is a better measure of student performance than Pi calculated by the authors. Also, the construction of Pi should be placed in the Methods section.

3. How do the authors define "privileged students" using the ESCS score? What is the cut-off point, and why is that point chosen as a cut-off?

4. Why did the authors use bootstraps (page 7) to calculate summary statistics? The PISA sample is supposed to be representative at the country level. They can use any statistical software to calculate the presented results. From what I read here, the results are the mean of test scores and the correlation between test scores and ESCS for different groups of students.

5. "Significance analysis" (page 7): It's not clear what the purpose of the significance analysis presented in this section is. If I understand correctly, the authors use significance analysis to test for the difference between mean scores across students from different economic backgrounds and/or whether the correlation coefficient between test scores and ESCS is statistically significant. If so, t-test and regressions are methods commonly used in social science for these purposes. If the authors decide to use modules and module node assignments, please cite a peer-reviewed published paper that has used this method to investigate similar issues in education or, in addition, present t-tests and regressions to show that the two approaches are similar.

6. "Building the network of countries and space nodes" (page 9): What is the purpose of using this method? What do the authors mean by "where the country has a larger fraction of its students than the average of all countries"? Do they mean a larger fraction of "privileged students" in the whole student population? If so, the statistic they need to calculate here is the deviation from the mean of the fraction of "privileged students", and that can be calculated using any statistical software.

7. Page 9: What is "Figure ??"

8. The authors need to provide the take-away point from each figure presented in the results section.

9. Figure 1 looks like the authors map PISA's deciles with ESCS scores, but I'm unsure.

10. The authors also conclude that students from privileged backgrounds performing better in Sweden results from school segregation. However, at least in the US, private schools are likely to select students with involved parents, and families with involved parents are likely to have higher test results regardless of ESCS status. Also, to attract students, private schools may provide more educational resources to students and have better teachers than public schools. Can you control for these different effects?

11. Cultures, such as China, where acceptance into top universities is dependent on doing well on exams, means that Chinese students in Public schools are also likely to attend after-school private schools. Is there a way that you can control for students who attend both public schools and private schools?

12. The authors' main conclusions are 1) students with a higher socio-economic status (SES) perform better than students with a lower SES, and 2) the extent of the performance gap varies across countries. The authors should reference more of the literature on higher SES students performing better than low SES students, which is well established in the social science literature. They should also reference the literature on human capital theories that provide explanations for the difference in academic achievement between students from different economic backgrounds.

Reviewer #2: The author analysed PISA data to explore the relationship between students' performance and economic status, searching for differences between public and private schools across countries. He applied network-based tools to construct and cluster a bipartite network formed by countries and portions of a bidimensional space representing students’ performances and economic status. Moreover, the author conducted complementary analysis to further the understanding of network clustering. He found a cluster of countries where less privileged students perform well, being this pattern an exception to the rule.

I think the manuscript is well written, well conducted and state-of-the-art in methodology. The results are robust and the interpretations seem appropriate to the methods and results. My complete inexperience in the topic prevent me to evaluate the relevance of the results, still from a complete layman’s point of view I found the results very interesting. I did not find any point that could be further improved but this two minor suggestions:

I had difficulty following Figure 1a. I think the lines represent isolines, but I am not sure. I would color or number the lines and provide a legend to facilitate understanding.

Please check “Figure ??” in lines 286 and 288

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Reviewer #1: No

Reviewer #2: No

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PISA data clusters reveal student and school inequality that affects results

PONE-D-21-35674R1

Dear Dr. Neuman,

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,

Jacob Freeman

Academic Editor

PLOS ONE

Additional Editor Comments (optional): Thank you for making excellent revisions and for clarifying concepts. The paper now meets Plos One criteria for publication. Best of luck on your future research.

Reviewers' comments: