PONE-D-22-15026The General Hopelessness Scale: Development of a measure for non-clinical samples PLOS ONE

Dear Dr. Ken Drinkwater,

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.

The manuscript is relevant and presents a new psychometric measure of hopelessness.

However, to accept the manuscript, it is recommended to consider the adjustments requested by the reviewers and, here, endorsed.

First, a much better way to guide your research is to address a scientific problem related to a relevant problem. Successful manuscripts usually answer at least one of the following questions: Is it a solution to an established and relevant problem? Is it an extension of a well-known technique? Is your contribution improving a technique or methodology? In any case, the literature review must clearly explain where your contribution fits.

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Third, for the methods and results sections, please refer to the APA recommendations. For more details, see: Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 3–25. https://doi.org/10.1037/amp0000191.

Also, consider updating the reference for the cutoffs used for CFA. For more details, see: van Laar, S., & Braeken, J. (2022). Caught off Base: A Note on the Interpretation of Incremental Fit Indices. Structural Equation Modeling: A Multidisciplinary Journal, 29(6), 935–943. https://doi.org/10.1080/10705511.2022.2050730.

Four, for data analysis and results.

1. Confirmatory Factor Analysis (CFA). Due to the ordinal nature of dataset and non-normal distribution, it is recommended to use a Weighted Least Squares Mean and Variance-Adjusted (WLSMV) estimator with polychoric matrices for performing the CFA. For more details, see: DiStefano, C., & Morgan, G. B. (2014). A Comparison of Diagonal Weighted Least Squares Robust Estimation Techniques for Ordinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 425–438. https://doi.org/10.1080/10705511.2014.915373

2. Rasch analysis. Due to the ordinal nature of the data set, it is necessary to fit the analysis to the gradation response model. Is a mathematical model family that deals with ordered polytomous categories. These ordered categories include ratings such as strongly disagree, disagree, agree, and strongly agree, which are used in the manuscript. For more details, see: Samejima, F. (2016). Graded response models. In Handbook of item response theory, volume one (pp. 123-136). Chapman and Hall/CRC. Furthermore, it is necessary to consider the discrimination ("a" parameter). Because the goal of most psychometric measures is to distinguish between examinees, items with higher discriminations ("a" parameter) are frequently regarded as better. Items with very low parameters provide minimal measurement information and might be considered for change or removal.

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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?

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Reviewer #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: No

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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

Reviewer #2: 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

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: In the manuscript "The General Hopelessness Scale: Development of a measure for non-clinical samples", the authors developed a new instrument to measure hopelessnes, aming to better discriminate between lower levels of hopelessness.

In general the topic of this manuscript is interesting for readers of this journal, the analyses are according to high standards, and the paper is written clear and nicely.

I have only two remarks:

- It would be good to also develop a short scale with 1-3 items per dimension to allow an effective and quick measurement in big panel surveys.

- Maybe an unpopular opinion and also not really constructive, but does the (scientific) world really need a measure of hopelessness? We have pretty good (and short) screening intruments for depression. Locus of control is also a very similar concept. So, what is the major advantage of measuring hopelessness? Why do we need to include this in our surveys?

Signed,

David Richter.

Reviewer #2: The manuscript reviewed here aimed at presenting the development of a novel measure of hopelesness, based on Beck's cognitive theory of depression, that could be used to non-clinical samples.

I do believe the studies presented are interesting and novel, showing a promising measure that could be used for research and also for therapeutic purposes, in order to understand an individual's beliefs regarding their own level of hope / hopelesness.

However, I also believe the manuscript is not ready for publication, as it needs further development in the introduction and to change the softwares used in the analyses.

Please, see my comments below.

ABSTRACT

* It doesn't present sample information. The abstract should contain information regarding sample, instruments and results.

INTRODUCTION

* Introduction is short on presenting the main concept of the paper, which is Hopelesness. Only one paragraph presents something about it before presenting the BHS. I believe a few more paragraphs explaining its importance and nomological network might be useful to locate the reader regarding the current study of the topic.

* The authors make interesting comments on the different structures observed in the literature and the limitations of using the BHS in non-clinical populations.

* The paper also justifies accordingly the need to develop a non-clinical hopelesness instrument, for which I commend the authors.

METHODS

* Participants and instruments are presented objectively, with all necessary information.

However, there is no information on how the study was presented to potential participants or how they came to know about the study. Was there any inclusion / exclusion criteria for participant selection? Procedures paragraph should include this information.

RESULTS

* I do believe the most recent algorithms for conducting a proper EFA are included in the FACTOR software, especially when involving the discussion regarding items on psychological instruments being ordinal and not scalar items. In this case, I suggest the authors change the software used in the EFA from SPSS to FACTOR.

* Also regarding the CFA, the AMOS software also doesn't include the most recent algorithms for these analyses. Softwares such as JASP, JAMOVI or the R language are much improved when compared to AMOS.

* For both comments here regarding the softwares, there might be no difference in the results, but we would be sure the analyses were conducted using the best softwares we have nowadays.

* Table 2 doesn't present the results the best way possible. We need to see all factor loadings in all dimensions to visualize its distribution.

* Why the term futility was used to describe "an individual's aversion to initiate or maintain new behaviours"? In current language, the term futility would be describing something trivial or pointless. I do understand the term emphasizes the individual's belief that changing or maintaning change is ineffective or fruitless. However, the definition is "aversion to initiate or maintain new behaviors". For me, the emphasis in this sentence is the aversion and not what the individual uses to justify their own aversion (the pointlessness of trying). This is only to think about as the terms we choose to describe the dimensions are important as they carry the meaning of the psychological construct underlying the items.

* Also, regarding the meaning of the items presented in Table 2 and their attributed dimension, I have a few doubts regarding their belonging to that definition. To exemplify, see below:

- Item 7 describes everything as pointless. Why is this item in the Social Comparison dimension and not in the Futility dimension, like the item 8?

- Item 9 is a clear case of Social Comparison, but why does the item 10 belong in the same dimension? Where is the social comparison in this case? Same question apply for items 11 and 16.

DISCUSSION

* I see a little bit of a discussion regarding the term 'futility' in the discussion session, which is very interesting. However, my comment remains on why calling it 'Futility' and not "Aversion to change" or "Lack of intrinsic motivation"?

* A more detailed paragraph on future studies regarding the potential use of the new measure is also lacking.

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Reviewer #1: Yes: David Richter

Reviewer #2: No

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The General Hopelessness Scale: Development of a measure for non-clinical samples

PONE-D-22-15026R1

Dear Dr. Drinkwater,

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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Academic Editor

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Additional Editor Comments (optional):

Thank you for revising

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

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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: Yes

Reviewer #2: 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

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 did a fine job with the revisionand addressed all my comments. At this point, I have no futher comments.

Reviewer #2: The new revised version of the paper adressed all my comments from the previous round. The only thing missing for me is the Figure 1, which did not appear in my pdf version of the manuscript. However, it is possible to infer its quality, considering the main numbers are described in the text.

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Reviewer #1: No

Reviewer #2: No

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