PONE-D-22-27061Development and Psychometric Evidence of University Students’ Academic Engagement Scale (USAES) in Mexican College StudentsPLOS ONE

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Reviewer #2: 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 study aims to propose and develop a new scale, called University Students’ Academic Engagement Scale (USAES), that is supposed to measure students’ engagement in specific contexts. While the authors have provided some explanations about the procedures of the development of the scale, and the decisions about the data-analytic procedures and results, I have some concerns or suggestions about those choices that are shown below. I also have some suggestions about how to justify the development of the scale instead of focusing on their perceived limitations of the existing scales that measure engagement. In addition, the authors should provide more details regarding the theoretical frameworks, conceptual models, and operational definitions of their measured construct. Please find my comments below.

Line 12: “there are important limitations” � Give some examples and explain what they are.

Line 16: “A sample of 992 Mexican college students…” � Is the purpose of this study focusing on developing the USAES for the Mexican population? Given that this is a newly developed scale, I suggest that the authors should clearly state their target population and how their sample could be regarded as a random, representative sample of the underlying population.

Line 23 “by gender…” � Why is only gender examined to support measurement invariance? How about the influence of other demographic factors or groups to provide a more complete picture about the potential measurement invariance across various groups of participants?

Line 46: “Moreover, regardless of theoretical conceptualizations, current scales tend to capture factors that affect engagement rather than indicators of engagement per se. Such scales comprise broadly worded items (e.g., I do like school) rather than worded to reflect engagement in particular situations.” � I am not an expert in the measurement of engagement, but I believe it is common that psychology researchers conceptualize and operationalize “general” and “situational” psychological constructs. Do you mean that the existing scales focus on measuring “general” engagement, and they do not measure “situational” engagement without providing a context? My quick search seems to show that many studies have examined situational engagement.

Katja Upadyaya, Patricio Cumsille, Beatrice Avalos, Sebastian Araneda, Jari Lavonen & Katariina Salmela-Aro (2021) Patterns of situational engagement and task values in science lessons, The Journal of Educational Research, 114:4, 394-403, DOI: 10.1080/00220671.2021.1955651

Lu, G., Xie, K., & Liu, Q. (2022). What influences student situational engagement in smart classroom: perception of learning environment and students’ motivation. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13204

Line 68: “Second, several scholars [23-26] have explored the test structure using EFA

69 and CFA, finding that NSSE's structure did not hold in their samples.” � Please provide details to support this claim. Do you mean their factor structures, loadings, and/or measurement invariance were not hold across “which” samples?

� It is relatively common that the factor structures of a published scale could vary (e.g., 2-factor, 3-factor, second-order factor, etc.) across different samples that are tested by independent researchers due to many factors (e.g., cultural differences). I believe a common practice for examining scale validation is that researchers would test and examine various factor structures that have been found in previous studies and conclude the one that has the best data fit to their study sample, with the goal to accumulate empirical evidence across replications.

Line 69: “it assesses many educational experiences not specifically engagement” � Give examples to support your view.

Line 100: “It seems that given engagement is fundamentally situational; it arises from the interaction of context and individual; scholars must ensure that the scales can capture student engagement within their specific contexts. That is, scales comprising items may not be appropriated, especially if they are interested in exploring how much engagement varies under specific contextual factors. Thus, scale items must be carefully worded to measure engagement in the specific context of higher education”

� I believe that this argument is over-stated. First, I am not 100% sure whether the existing literature does not offer any scale that measurement “situational engagement within a context” (I believe there should be some). Second, even if no scale is currently available, previous research may focus on measuring “general” engagement that is conceptualized as a general, psychological trait (e.g., extroversion/introversion of a person based on the Big Five personality). Hence, measuring the construct of “general” engagement should not be regarded as a criticism leading to the current study. Third, I found that there are some other places in which the authors argue that most current, widely employed engagement scales (e.g., UWES-S; lines 87 to 95) are less than optimal because of the mixed results regarding their factor structures. Indeed, validating a scale across different samples in different research settings or labs adheres to the principle of accumulating empirical evidence in science, and it is relatively common among different researchers to observe different factor structures of the same scale (e.g., due to many factors such as culture differences, translation of the scale questions, etc.). In a hypothetical scenario, when future researchers intend to test and validate the proposed USAES based on other samples, it is likely that the USAES would also be found with mixed results (e.g., various factor structures). Or, stated differently, the existing literature does not provide any empirical evidence that the USAES has been comprehensively tested and validated (other than the current sample of n = 992 university students in Mexico) to possess more appropriate psychometric properties (e.g., the same factor structure, high reliability, etc.) than most other current scales (e.g., UWES-S).

� In my view, there is no need to criticize “how bad” the current scales are in measuring engagement, and hence, the authors would like to propose and develop a new one. Rather, the tone should be softened, and the authors could directly state that their goal is to develop an engagement scale that measure students’ engagement within specific contexts based on their theoretical framework and conceptual models. Indeed, their paragraphs under the heading: “Theoretical Framework for Measurement Academic Engagement” is a bit misleading because I anticipate that the authors should state their theoretical framework for engagement. For example, what are the existing psychological theories that conceptualize “engagement in a specific context”? How do those theories explain the behavioral, emotional, and cognitive aspects of engagement? Why does the authors select or focus on one theory (if any) that directs them to develop the proposed USAES? After selecting a particular theoretical framework, what conceptual models do they propose (e.g., a conceptual diagram including all the paths that link the antecedents such as academic rigor; line 137) and outcomes?

� Regarding “academic rigor” (lines 136 – 139), this section should be expanded. For example, what is the literature review about academic rigor? Why is only one antecedent or predictor selected in this study to predict engagement?

� In sum, the current literature review focuses on discussing the authors’ perceived weaknesses of the existing scales that measure engagement without any details (e.g., theoretical frameworks, conceptual models, operational definition, etc.) about their proposed construct. Indeed, I am not 100% sure about the concept or meaning of the proposed construct (should it be “student engagement in specific contexts”? If that is the case, what types of students (e.g., undergraduate, graduate, university students, or others) do they focus on? What “specific contexts” do they refer to?

Line 124 – 125: “Another important issue that has not been addressed in literature is the need to prove that engagement measurement functions similarly across males and females.” �This statement assumes that the variable is gender or a dichotomous view of gender with the categories of males and females only. Line 128: “…found women are equally engaged as their male counterparts…” � The word “women” appears that the authors refer to the “biological sex” of participants. There is some confusion about whether the authors refer to gender, e.g., males/females; gender orientation/tendency that is different from biological sex (e.g., men/women).

Line 146: “members from three public universities located in the north (Sonora)”

� How many public universities are there in Mexico? Does the number of 3 ensure that this is a representative sample? As noted above, there is no need to over-state the representativeness, as no study is perfectly or truly based on random sampling. The authors could directly mention their sampling procedure instead of stating that having "a presentative sample from students all over the country".

Line 161: “From these focus group conversations, 25 indicators of academic engagement” � This part misses the details of the qualitative data analysis. E.g., coding, thematic analysis, inter-coder reliability, etc. Please provide the details.

Line 162: “Then, a group of eight experts in higher education” � What are their background, and who are they?

Line 167: “The review by experts ensured high quality and the relevance of the USAES items in alignment with the context and reality of Mexican higher education classrooms”. � This statement seems to be over-stated without the details of the processes that provide scientific evidence and support regarding why and how the experts could ensure “high quality” of the USAES items. “This process led to a considerable reduction of the item pool. In total, 11 of 25 original items of the USAES were removed; only 14 items accomplished a content validity index (CVI) greater than .80 [57]. � Similarly, no evidence has been provided regarding the criteria and processes that delete the 11 items (e.g., based on what approach/method/criteria?)

Line 213: “I do attend all my classes, labs, practices” � Were the items presented in English or Mexican language?

Line 244: “using robust weighted least squares (DWLS)” Why did the authors use DWLS instead of the default estimator maximum likelihood with robust standard error in MPlus? The authors should provide their reasons for choosing a particular estimator.

Bandalos, D. L. (2014). Relative performance of categorical diagonally weighted least squares and robust maximum likelihood estimation. Structural Equation Modeling, 21(1), 102–116. https://doi.org/10.1080/10705511.2014.859510

Li, C. H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavioral Research Methods, 48, 936–949. https://doi.org/10.3758/s13428-015-0619-7

Line 270: “omega = .70” � Do the authors refer to omega-hierarchical or omega-total? Also, .70 is not high, and it is interpreted as marginal to reasonable reliability. This finding may also suggest that the proposed USAES may not necessarily have better reliability than current scales such as USAES. Or the authors should at least provide the reliability coefficients of other current scales for comparison.

Line 314: “The goodness of fit of second-order factor model (Model B) was not statistically better (ΔSBX2 = 8.54, Δdf = 1, p = .003; ΔBIC = 6.83) than” � It states: “was not statistically better”, but the p value is .003 which is significant at the .05 level (or p < .05).

Reviewer #2: The manuscript is well written. The author/s clearly outlined the justification for the conducted research (i.e., development of the Academic Engagement Scale in Mexican context). Both the theoretical rationale and the presented results make a valuable contribution: provide a new “theoretically-grounded, culturally sensible, and robust” psychometric tool and explain the context of its use in future research as well as educational practice. The study is well designed, and the statistical analyses properly selected and reported. Please find my few minor comments below.

1. The author/s defined three facets of an academic engagement, i.e., behavioral, emotional, and cognitive. However, in the theoretical part, they do not refer to the most common model, known as the ABC model of attitudes (see Ostrom, 1969) that includes these three components. Then it is necessary to specify to what extent academic engagement is derived from the theory of attitudes. This clarification may shed more light on the understanding of this phenomenon.

2. The author/s undertook to study the measurement invariance of the USAES across genders, however, it would be advisable to comment on the equivalence of this tool in the context of its use in different types of schools and in different cultures. Can the developed tool be adapted to other countries in the future? Is it possible to adapt the tool to the conditions of other schools (secondary, primary)? What are the authors' recommendations?

3. Did the authors use the robust Weighted Least Squares (robust WLS) or DWLS (Diagonally Weighted Least Squares) estimation? I am belief that the choice of the DWLS estimator was not the most optimal one but probably robust WLS was used in the study. A brief justification for the choice of the estimator is needed.

4. Since the factor structure showed the second-order engagement factor, the general score should be included in the latent means comparison by gender as well as in the correlations between academic engagement dimensions and academic rigor.

5. The results presented in Table 6 show that affective academic engagement and cognitive academic engagement correlate more strongly with academic rigor than with the behavioral component of academic engagement. Could this have implications for distinguish the three components of the academic engagement.

Ostrom, T. M. (1969). The relationship between the affective, behavioral, and cognitive components of attitude. Journal of Experimental Social Psychology, 5(1), 12–30. https://doi.org/10.1016/0022-1031(69)90003-1

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Reviewer #1: No

Reviewer #2: Yes: Paweł Jurek

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PONE-D-22-27061R1Development and Psychometric Evidence of University Students’ Academic Engagement Scale (USAES) in Mexican College StudentsPLOS ONE

Dear Dr. Parra-Pérez,

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.

Please submit your revised manuscript by May 18 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Ali B. Mahmoud, Ph.D.

Academic Editor

PLOS ONE

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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

Reviewer #2: 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

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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: No

Reviewer #2: No

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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

Reviewer #2: Yes

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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: I appreciate your flexibility and willingness in incorporating my previous suggestions to your revised study, which has improved a lot (e.g., theoretical approach, sampling and interview procedures, clarity about the choice of the data analysis, etc.)! I have some minor comments below.

Lines 113 – 115: “Although prior research indicates that levels of student engagement may vary significantly by ‘biological sex’ [46-48], some research continues to report mixed results. Whereas a group of scholars [49] found that females are equally engaged as their male counterparts”

� Should “biological sex” be changed to “gender”, as you discuss about the differences between males and females?

Lines 335 – 338: � Which omega coefficient did you report (e.g., omega-total, omega hierarchical or others; Trizano-Hermosilla et al., 2021)? Please label your symbol more precisely.

Trizano-Hermosilla, I., Gálvez-Nieto, J. L., Alvarado, J. M., Saiz, J. L., & Salvo-Garrido, S. (2021). Reliability estimation in multidimensional scales: Comparing the bias of six estimators in measures with a bifactor structure. Frontiers in psychology, 12, 508287. https://doi.org/10.3389/fpsyg.2021.508287

Line 310 – 313: “In order to interpret such a correlation, we adopted the guides offered by Cohen [81], which suggest that an r of .10…”

� While it is common that researchers use some thresholds (or “t-shirt” sizes) to interpret an effect size, recent studies (e.g., Bakker, et al., 2019) have suggested that this is an inappropriate or inadequate approach (e.g., the criteria for small, medium, and large effect sizes should vary across different disciplines, etc.). I suggest that you should mention about that in the limitation section.

Bakker, A., Cai, J., English, L.D., Kaiser, G., Mesa, V., & Van Dooren, W. (2019). Beyond small, medium, or large: points of consideration when interpreting effect sizes. Educational Studies in Mathematics, 102, 1 - 8.

Line 314: “The goodness of fit of second-order factor model (Model B) was not statistically better (ΔSBX2 = 8.54, Δdf = 1, p = .003; ΔBIC = 6.83) than” � It states: “was not statistically better”, but the p value is .003 which is significant at the .05 level (or p < .05).

Response

Based on structural modeling literature, we adopted .001 level to compared model fit.

� Can you provide the citations and discuss the reasons for using .001 as the level of significance for comparing nested models? Using a more stringent .001 (instead of .05) level for testing the differences between two models seem to decrease the likelihood to signal any significant differences of the fit between the two models. In other words, it is more likely to conclude that the two models have the “same” fit (or the two models are identical in terms of their goodness-of-fit levels to your data), unless they have very or super large differences in the population.

Thank you for the opportunity to review this study!

Reviewer #2: I appreciate the Authors’ efforts to improve the manuscript. I think the Authors efficiently responded to my comments. Thus, I find the revised manuscript to be substantially improved and I endorse its publication in the PLOS ONE.

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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: No

Reviewer #2: Yes: Paweł Jurek

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Development and Psychometric Evidence of University Students’ Academic Engagement Scale (USAES) in Mexican College Students

PONE-D-22-27061R2

Dear Dr. Parra-Pérez,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Ali B. Mahmoud, Ph.D.

Academic Editor

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Additional Editor Comments (optional):

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

Reviewer #2: 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

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: 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: No

Reviewer #2: Yes

**********

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

Reviewer #2: Yes

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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: The authors have appropriately addressed all my previous comments. Thank you and look forward to seeing the published version!

Reviewer #2: I find the revised manuscript to be substantially improved and I endorse its publication in the PLOS ONE.

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Reviewer #1: No

Reviewer #2: Yes: Paweł Jurek

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