index in phd

What Is Good H-Index? H-Index Required For An Academic Position

In the academic world, the h-index score stands as a pivotal metric, gauging the impact and breadth of a researcher’s work. Understanding what constitutes a good h-index is crucial for academics at all stages, from budding PhD students to seasoned professors.

This article looks into the h-index, exploring what scores are considered impressive across various disciplines and career stages.

• PhD Student: An h-index between 1 and 5 is typical for PhD students nearing the end of their program, reflecting their early stage in academic publishing.
• Postdoc and Assistant Professor: Early career researchers like postdoctoral fellows or assistant professors often find an h-index around 5 to 10 impressive, indicating a solid start in their respective fields.
• Associate Professor: At this more advanced stage, an h-index of 10 or more is generally expected, reflecting a consistent record of impactful research.
• Full Professor: For full professors, an h-index of 15 or higher is often seen, indicating a long and impactful career in research and academia.

How To Calculate Your H-Index Score?

In the academic world, the h-index score is a critical metric, essentially acting like a report card for scholars.

The h-index is a measure of a researcher’s productivity and impact. H-index was designed to assess the number of papers published and the number of citations each paper receives. 

Now that you know what is a h-index score, you may now wonder if you can find out your own. Good thing is that platforms like Google Scholar or Web of Science can come in handy.

They track your number of publications and the number of times those publications are cited, crunching these numbers into your h-index.

This number can vary based on the field and years of research experience. A full professor might be expected to have a higher h-index, reflecting more years of impactful research.

Google Scholar

To find out your h-index score from Google Scholar, you can follow the steps below:

• Create a Google Scholar Profile : If you don’t already have one, go to Google Scholar and create a profile. Fill in your academic details and affiliations.
• Add Publications : Ensure all your research publications are listed in your profile. You can add them manually or import them if they are already available on Google Scholar.
• Verify your Publications : Make sure the publications listed are indeed yours, as sometimes publications from other authors with similar names might appear.
• Check the Citations Section : Once your profile is complete and updated, look for the ‘Citations’ section on your profile page. This is usually located at the top and easy to spot.
• Find Your H-Index : In the Citations section, you will see your h-index listed among other citation metrics like the total number of citations and the i10-index.

Web Of Science

To find out your h-index score from Web Of Science, you can follow the steps below:

• Access Web of Science : Go to the Web of Science website. Access may require an institutional login, depending on your affiliation.
• Search for Your Name : Use the author search function to find your publications. Ensure you search with variations of your name if you’ve published under different names or initials.
• Create a Citation Report : Once your publications are listed, select them and create a citation report. This option is typically found above the list of your publications.
• View Your H-Index : In the citation report, your h-index will be displayed. This number is calculated based on the total number of papers you’ve published and the number of citations each paper has received.

What H-Index Is Considered Good For A PhD Student?

For a PhD student, the world of academic metrics can be daunting, especially when it comes to the h-index, a measure that intertwines the number of publications with their citation impact.

So, what h-index score should you, as a PhD student, aim for?

A “good” h-index can vary based on your field of study and the stage of your PhD program.

Generally, for PhD students, a lower h-index is expected and completely normal. You’re just beginning your journey in academic publishing.

An h-index between 1 and 5 might be typical for students nearing the end of their PhD. This means you have 1 to 5 publications that have been cited at least 1 to 5 times, respectively.

Your h-index can be calculated using tools like Google Scholar or Web of Science. These platforms track your published papers and the number of citations each receives.

As a PhD student, your focus should be on publishing quality research in reputable journals, as this will gradually increase your h-index.

Remember, while a higher h-index is beneficial for future academic positions, it’s not the only metric that matters. Your research’s quality, relevance, and impact in your field are equally important. A single highly influential paper might open more doors than several less impactful ones.

What Are Good H-Index Required For An Academic Position?

your h-index can be as crucial as your research itself. This metric, a blend of productivity and impact, is often scrutinized by hiring committees.

But what number should you aim for? A good h-index varies by field and career stage.

PostDoc, Assistant Professors

For early career researchers, like postdoctoral fellows or assistant professors, an h-index around 5 to 10 is often impressive.

It shows you’ve made a mark in your field, with a number of papers that have been cited at least that many times. 

Associate Professor, Full Professor

In more senior roles, such as a tenured associate professor or full professor, expectations rise.

Here, an h-index of 10 or 15 might be the minimum, with higher numbers not uncommon.

This single number, while important, doesn’t tell the whole story. A young researcher might have a lower h-index simply due to less time in the field. Moreover, some fields tend to have higher citation rates, which can inflate h-index scores.

It’s wise to keep an eye on your h-index, especially if you’re eyeing:

• Competitive academic positions,
• Research funding
• Collaboration opportunities.

Improving your h-index involves not just publishing papers, but ensuring they are of high quality and relevance, increasing the likelihood of citations.

In sum, a good h-index is one that matches your career stage and field, reflecting both the quantity and impact of your work. However, it’s not the sole measure of your worth as a researcher.

The breadth and depth of your contributions, beyond just citation counts, also paint a vivid picture of your academic and scientific impact.

What Metric Influences H-Index Score?

Your h-index score is influenced by several key factors:

• Number of Publications : The more papers you publish, the greater the potential for citations. It’s a numbers game, but quality over quantity should be your mantra. High-caliber papers in respected journals often garner more attention and citations.
• Citations Per Publication : Your h-index heavily relies on how often your papers are cited. Even if you have a plethora of publications, your h-index won’t shine if they’re seldom cited.
• Years of Research Experience : A young researcher might have a lower h-index compared to a full professor, who has had more time to build their citation record.
• Research Field : The h-index varies widely across disciplines. Fields with rapid publication and citation rates like biomedical sciences often see higher h-index scores than, say, humanities. So, a good h-index in one field might be considered low in another.
• Access to Research Collaborations : Collaborations can boost your h-index. Working with other researchers, can increase the visibility and citation potential of your papers. However, too many authors on a single paper might dilute the perceived contribution of each.

Remember, while a high h-index can be indicative of a significant academic impact, it’s not the sole measure of your scientific worth. It’s a good idea to give your h-index some consideration, but also focus on the broader spectrum of your academic contributions.

How To Increase H-Index Score?

Increasing your h-index, a metric reflecting the impact and productivity of your academic work, is a strategic goal for many researchers.

This single number, representing the intersection of the quantity of your publications and their citation impact, can play a pivotal role in securing research grants and academic positions.

To boost your h-index, focus on publishing quality research in well-regarded journals. A paper published in a respected journal is more likely to be cited, and each citation nudges your h-index upwards.

For example, if you’re an assistant professor with an h-index of 5, aiming for journals with high visibility in your field can help you reach a higher h-index, making you more competitive for positions like associate or full professor.

Collaboration is another key strategy. Co-authoring with established researchers can increase the reach and citation potential of your papers.

This, however, comes with a caveat: the more number of authors on a paper, the more diluted your perceived contribution might be. Aim for a balance in co-authorship.

Active engagement in the academic community also matters. Increase citations on your work by:

• Presenting at conferences,
• networking, and
• promoting your work on platforms like Google Scholar or Web of Science.

Remember, the h-index varies by field and career stage. A good h-index for a young researcher might be 10, while more senior academics might aim for higher numbers. Using databases like Google Scholar, you can track your number of cited publications and calculate your h-index.

While a higher h-index can bolster your academic profile, it’s not the sole indicator of your scholarly worth – low h-index score is not a dealbreaker in many cases. It’s wise to consider it alongside other measures of your academic and scientific impact.

Good H-Index Score May Vary

A good h-index score is relative, varying across academic fields and career stages. While it offers a valuable snapshot of a researcher’s impact and productivity, it’s important to view it as one part of a larger picture.

Aspiring for a higher h-index should go hand in hand with maintaining the quality and relevance of research. Ultimately, the h-index is a useful tool, but it’s the depth and innovation of your work that truly define your academic legacy.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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How to make an index for your book or dissertation

Dear Readers. Shaun Lehmann, Katherine Firth (of the Research Voodoo blog ) and I are currently in the process of writing a new book for Open University Press called ‘Writing Trouble’. ‘Writing Trouble’ will help you diagnose and treat your thesis writing problems.

The proposed book evolved out of our work on the Thesis Bootcamp program , a writing intervention originally designed by Peta Freestone and Liam Connell . Over the years all of us have been running our own bootcamps we have met hundreds of students struggling to put their final thesis draft together. These students have supervisors who are clearly great researchers, but cannot give good feedback on writing. The book works backwards from the confusing feedback students have showed us.

Part of our process with this new book is to test out some of our text on our audience – you. Here is part of another chapter from our section “Where’s your discussion section?” where we deal with the purpose of the conventional ‘bits’ of a thesis and how to treat them. This piece of writing on indexing relates to a previous piece I wrote on the Whisperer about how to do a glossary . It’s the first draft, so your feedback is appreciated!

If you’d like to know more about the book before it’s published, you can sign up for our writing trouble mailing list .

The index is the elder sibling of the glossary , who has grown up, moved to the big city and started doing drugs. Anyone who has been asked to write one will tremble a little in their boots, at least the first time. Basically, an index is a quick look up list of terms that appear in your dissertation or book. In a similar way to the glossary, an index serves a rhetorical as well as a communicative role by throwing a spotlight on the parts of your book that will be most interesting and useful to the reader.

Indexing is an even more labourious process than making a glossary, but the return on investment is definitely worth it. Beyond the academic examination context, a good index is a vital tool in convincing a reader whether or not to read (or buy) your book. How often have you flipped to the index of the book to see if there’s enough on the topic you are interested in to warrant the effort? That’s right – almost every time.

Until this book, only Inger had experience of writing an index and she did a pretty horrible job of it. Here is what she learned.

Step one: Develop some useful themes

To begin, you need to think about why a reader might want to buy or read your book in the first place. You are not writing a novel, so being practical is not a bad place to start. As a thought exercise, try to think about the kind of problems that your readers are looking to solve. Think of words or phrases to represent these problems and you have a rough list of themes.

Inger’s previous book “How to be an academic” was a practical guide to surviving in academia, especially if you are a precariously employed academic. She started by generating a list of things like “making money”, “dealing with assholes”, “writing quickly” and so on. She then tried to think about the themes she thought were important, to give the index reader a sense of the broad range of topics in the book. This generated terms like “networking”. These themes guided the next step: identifying the areas of text where these themes were discussed.

Step Two: find the chunks of text that relate to the themes

The next step is the absolute worst part of the whole process, so prepare yourself. To get to a list-y looking thing, one must read a text that one is incredibly sick of reading by now with a forensic eye. The purpose of this step is to take note of the various manifestations of your themes in the book and make a note of their location. DO NOT DO THIS STEP UNTIL YOU HAVE PRINTER READY TEXT OR YOUR PAGE NUMBERS WILL BE WRONG.

Each time you find that theme in chunk of text, think about a short word or phrase that might relate to that theme and note the page number. Inger’s first pass looked something like this:

Acronyms, value of                                         124 – 125

Arrogance                                                       50 – 55

‘Backstage work’                                            226, 236

Bookshelves                                                    306

Cleverness                                                       46, 49, 250 – 251, 255 – 257

Cultural Capital                                               46 – 47, 89 – 90, 245

Dinner Parties                                                 56, 60, 64

Competition                                                    260

Fashion                                                            85 – 90, 306

Gift economies                                                253 – 254

Hiring practices                                               62, 229 – 236

Love of the work                                             18, 76, 264, 288 – 291

Migrants                                                         56 – 60

Salaries                                                           31, 222

‘service’                                                           101

The new normal                                              39, 229, 231

Academia as a Bad Boyfriend                                           16 – 19, 32 – 33, 36, 231

Academic journals, questionable practices of                  156 – 162

Academic hunger games                                                   13, 229

ADHD                                                                                67

Amabile, Tessa                                                                  46

Aaron, Rachael                                                                  198

Architecture as a profession                                             28, 218

Baby Boomers                                                                   283

Becker, Howard                                                                125, 153 – 154, 193, 195 – 196

Bullying                                                                             52, 54 – 55

Blogging and social media

The purpose of the Thesis Whisperer blog     9

Time implications of blogging                         12, 177

Starting blogging                                            22

Mark’s simple rules of blogging                     38

Safe Spaces?                                                   48, 267

Writing posts                                                  82, 263 – 264

Value of sharing for your career                    112, 220, 303 – 304

As open access publishing                               154, 159, 220 – 222

Enjoyment                                                       256, 263

Mainstream media shit storms                      268 – 269

Social media shit storm                                  284 – 285

At a certain point in making this list, Inger gave up trying to keep it tidy and started using Nvivo, a text analysis software. This worked well, but she doesn’t recommend using this software unless you have the skills; there’s a big learning curve and you have a book to deliver.

Step Three: throw out the themes

When Inger’s publisher got this index, carefully compiled over a couple of weekends, she smiled kindly, thanked Inger for the effort and gave it straight to a professional. When it came back, it looked completely different. In Inger’s version, dinner parties appeared under the theme of ‘academic’: a vague sort of category, in the final version it appeared under D, you know – for dinner party.

The lesson? When you are generating an alphabetical list, it’s best to bear in mind the alphabet. Inger was close, she just needed to throw away the themes and arrange the list of key words in alphabetical order. The final touch would be to try to think of words that are related to each other and put “see also” under them.

Job done, no drugs necessary. Except, maybe – coffee.

This is how I did an index, but I’m sure there are more elegant and sophisticated techniques. Have you ever done one? Do you have tricks to share? Love to hear about them in the comments!

Related posts

Sign up for the ‘writing trouble’ book news mailing list.

Buy “How to be an academic”

Enter the Glossators

Other ‘first draft’ posts from the Writing Trouble Series

The vagueness problem in academic writing

Academia is a passive agressive, middle class dinner party

Your thesis is the map, not the journey

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The Thesis Whisperer is written by Professor Inger Mewburn, director of researcher development at The Australian National University . New posts on the first Wednesday of the month. Subscribe by email below. Visit the About page to find out more about me, my podcasts and books. I'm on most social media platforms as @thesiswhisperer. The best places to talk to me are LinkedIn , Mastodon and Threads.

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Reference management. Clean and simple.

What is the h-index?

A simple definition of the h-index

Step-by-step outline: how to calculate your h-index, why it is important for your career to know about the h-index, can all your academic achievements be summarized by a single number, frequently asked questions about h-index, related articles.

An h-index is a rough summary measure of a researcher’s productivity and impact. Productivity is quantified by the number of papers, and impact by the number of citations the researchers' publications have received.

The h-index can be useful for identifying the centrality of certain researchers as researchers with a higher h-index will, in general, have produced more work that is considered important by their peers.

The h-index was originally defined by J. E. Hirsch in a Proceedings of the National Academy of Sciences article as the number of papers with citation number ≥ h . An h-index of 3 hence means that the author has published at least three articles, of which each has been cited at least three times.

The h-index can also simply be determined by charting the article's citation counts. The h-index is then determined by the interception of the chart's diagonal with the citation data. In this case, there are 3 papers that are above the diagonal, and hence the h-index is 3.

The definition of the h-index comes with quite a few desirable features:

• First, it is relatively unaffected by outliers. If e.g. the top-ranked article had been cited 1,000 times, this would not change the h-index.
• Second, the h-index will generally only increase if the researcher continues to produce good work. The h-index would increase to 4 if another paper was added with 4 citations, but would not increase if papers were added with fewer citations.
• Third, the h-index will never be greater than the number of papers the author has published; to have an h-index of 20, the author must have published at least 20 articles which have each been cited at least 20 times.
• Step 1 : List all your published articles in a table.
• Step 2 : For each article gather the number of how often it has been cited.
• Step 3 : Rank the papers by the number of times they have been cited.
• Step 4 : The h-index can now be inferred by finding the entry at which the rank in the list is greater than the number of citations.

Here is an example of a table where articles have been ranked by their citation count and the h-index has been inferred to be 3.

Luckily, there are services like Scopus , Web of Science , and Google Scholar that can do the heavy lifting and automatically provide the citation count data and calculate the h-index.

The h-index is not something that needs to be calculated on a daily basis, but it's good to know where you are for several reasons. First, climbing the h-index ladder is something worth celebrating. If it's worth opening a bottle of champagne or just getting a cafe latte, that's up to you, but seriously take your time to celebrate this achievement (there aren't that many in academia). But more importantly, the h-index is one of the measures funding agencies or the university's hiring committee calculate when you apply for a grant or a position. Given the often huge number of applications, the h-index is calculated in order to rank candidates and apply a pre-filter.

Of course, funding agencies and hiring committees do use tools for calculating the h-index, and so can you.

It is important to note that depending on the underlying data that these services have collected, your h-index might be different. Let's have a look at the h-index of the well-known physicist Stephen W. Hawking to illustrate it:

So, if you are aware of a number of citations of your work that are not listed in these databases, e.g. because they are in conference proceedings not indexed in these databases, then please state that in your application. It might give your h-index an extra boost.

➡️ Learn more: What is a good h-index?

Definitely not! People are aware of this, and there have been many attempts to address particular shortcomings of the h-index, but in the end, it's just another number that is meant to emphasize or de-emphasize certain aspects of the h-index. Anyway, you have to know the rules in order to play the game, and you have to know the rules in order to change them. If you feel that your h-index does not properly reflect your academic achievements, then be proactive and mention it in your application!

An h-index is a rough summary measure of a researcher’s productivity and impact . Productivity is quantified by the number of papers, and impact by the number of citations the researchers' publications have received.

Google Scholar can automatically calculate your h-index, read our guide How to calculate your h-index on Google Scholar for further instructions.

Even though Scopus needs to crunch millions of citations to find the h-index, the look-up is pretty fast. Read our guide How to calculate your h-index using Scopus for further instructions.

Web of Science is a database that has compiled millions of articles and citations. This data can be used to calculate all sorts of bibliographic metrics including an h-index. Read our guide How to use Web of Science to calculate your h-index for further instructions.

The h-index is not something that needs to be calculated on a daily basis, but it's good to know where you are for several reasons. First, climbing the h-index ladder is something worth celebrating. But more importantly, the h-index is one of the measures funding agencies or the university's hiring committee calculate when you apply for a grant or a position. Given the often huge number of applications, the h-index is calculated in order to rank candidates and apply a pre-filter.

Thesis and Dissertation Guide

• « Thesis & Dissertation Resources
• The Graduate School Home

• Introduction

Copyright Page

Dedication, acknowledgements, preface (optional), table of contents.

• List of Tables, Figures, and Illustrations

List of Abbreviations

List of symbols.

• Non-Traditional Formats
• Font Type and Size
• Spacing and Indentation
• Tables, Figures, and Illustrations
• Formatting Previously Published Work
• Internet Distribution
• Open Access
• Registering Copyright
• Using Copyrighted Materials
• Use of Your Own Previously Published Materials
• Submission Steps
• Submission Checklist
• Sample Pages

I. Order and Components

Please see the sample thesis or dissertation pages throughout and at the end of this document for illustrations. The following order is required for components of your thesis or dissertation:

• Dedication, Acknowledgements, and Preface (each optional)
• Table of Contents, with page numbers
• List of Tables, List of Figures, or List of Illustrations, with titles and page numbers (if applicable)
• List of Abbreviations (if applicable)
• List of Symbols (if applicable)
• Introduction, if any
• Main body, with consistent subheadings as appropriate
• Appendices (if applicable)
• Endnotes (if applicable)
• References (see section on References for options)

Many of the components following the title and copyright pages have required headings and formatting guidelines, which are described in the following sections.

Please consult the Sample Pages to compare your document to the requirements. A Checklist is provided to assist you in ensuring your thesis or dissertation meets all formatting guidelines.

The title page of a thesis or dissertation must include the following information:

• The title of the thesis or dissertation in all capital letters and centered 2″ below the top of the page.
• Your name, centered 1″ below the title. Do not include titles, degrees, or identifiers. The name you use here does not need to exactly match the name on your university records, but we recommend considering how you will want your name to appear in professional publications in the future.

Notes on this statement:

• When indicating your degree in the second bracketed space, use the full degree name (i.e., Doctor of Philosophy, not Ph.D. or PHD; Master of Public Health, not M.P.H. or MPH; Master of Social Work, not M.S.W. or MSW).
• List your department, school, or curriculum rather than your subject area or specialty discipline in the third bracketed space. You may include your subject area or specialty discipline in parentheses (i.e., Department of Romance Languages (French); School of Pharmacy (Molecular Pharmaceutics); School of Education (School Psychology); or similar official area).
• If you wish to include both your department and school names, list the school at the end of the statement (i.e., Department of Pharmacology in the School of Medicine).
• A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Public Policy.
• A thesis submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Science in the School of Dentistry (Endodontics).
• A thesis submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Science in the Department of Nutrition in the Gillings School of Global Public Health.
• A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the School of Education (Cultural Studies and Literacies).
• The words “Chapel Hill” must be centered 1″ below the statement.
• One single-spaced line below that, center the year in which your committee approves the completed thesis or dissertation. This need not be the year you graduate.
• Approximately 2/3 of the way across the page on the right-hand side of the page, 1″ below the year, include the phrase “Approved by:” (with colon) followed by each faculty member's name on subsequent double-spaced lines. Do not include titles such as Professor, Doctor, Dr., PhD, or any identifiers such as “chair” or “advisor” before or after any names. Line up the first letter of each name on the left under the “A” in the “Approved by:” line. If a name is too long to fit on one line, move this entire section of text slightly to the left so that formatting can be maintained.
• No signatures, signature lines, or page numbers should be included on the title page.

Include a copyright page with the following information single-spaced and centered 2″ above the bottom of the page:

© Year Author's Full Name (as it appears on the title page) ALL RIGHTS RESERVED

This page immediately follows the title page. It should be numbered with the lower case Roman numeral ii centered with a 1/2″ margin from the bottom edge.

Inclusion of this page offers you, as the author, additional protection against copyright infringement as it eliminates any question of authorship and copyright ownership. You do not need to file for copyright in order to include this statement in your thesis or dissertation. However, filing for copyright can offer other protections.

See Section IV for more information on copyrighting your thesis or dissertation.

Include an abstract page following these guidelines:

• Include the heading “ABSTRACT” in all capital letters, and center it 2″ below the top of the page.
• One double-spaced line below “ABSTRACT”, center your name, followed by a colon and the title of the thesis or dissertation. Use as many lines as necessary. Be sure that your name and the title exactly match the name and title used on the Title page.
• One single-spaced line below the title, center the phrase “(Under the direction of [advisor's name])”. Include the phrase in parentheses. Include the first and last name(s) of your advisor or formal co-advisors. Do not include the name of other committee members. Use the advisor's name only; do not include any professional titles such as PhD, Professor, or Dr. or any identifiers such as “chair” or “advisor”.
• Skip one double-spaced line and begin the abstract. The text of your abstract must be double-spaced and aligned with the document's left margin with the exception of indenting new paragraphs. Do not center or right-justify the abstract.
• Abstracts cannot exceed 150 words for a thesis or 350 words for a dissertation.
• Number the abstract page with the lower case Roman numeral iii (and iv, if more than one page) centered with a 1/2″ margin from the bottom edge.

Please write and proofread your abstract carefully. When possible, avoid including symbols or foreign words in your abstract, as they cannot be indexed or searched. Avoid mathematical formulas, diagrams, and other illustrative materials in the abstract. Offer a brief description of your thesis or dissertation and a concise summary of its conclusions. Be sure to describe the subject and focus of your work with clear details and avoid including lengthy explanations or opinions.

Your title and abstract will be used by search engines to help potential audiences locate your work, so clarity will help to draw the attention of your targeted readers.

You have an option to include a dedication, acknowledgements, or preface. If you choose to include any or all of these elements, give each its own page(s).

A dedication is a message from the author prefixed to a work in tribute to a person, group, or cause. Most dedications are short statements of tribute beginning with “To…” such as “To my family”.

Acknowledgements are the author's statement of gratitude to and recognition of the people and institutions that helped the author's research and writing.

A preface is a statement of the author's reasons for undertaking the work and other personal comments that are not directly germane to the materials presented in other sections of the thesis or dissertation. These reasons tend to be of a personal nature.

Any of the pages must be prepared following these guidelines:

• Do not place a heading on the dedication page.
• The text of short dedications must be centered and begin 2″ from the top of the page.
• Headings are required for the “ACKNOWLEDGEMENTS” and “PREFACE” pages. Headings must be in all capital letters and centered 2″ below the top of the page.
• The text of the acknowledgements and preface pages must begin one double-spaced line below the heading, be double-spaced, and be aligned with the document's left margin with the exception of indenting new paragraphs.
• Subsequent pages of text return to the 1″ top margin.
• The page(s) must be numbered with consecutive lower case Roman numerals (starting with the page number after the abstract) centered with a 1/2″ margin from the bottom edge.

Include a table of contents following these guidelines:

• Include the heading “TABLE OF CONTENTS” in all capital letters, and center it 2″ below the top of the page.
• Include one double-spaced line between the heading and the first entry.
• The table of contents should not contain listings for the pages that precede it, but it must list all parts of the thesis or dissertation that follow it.
• If relevant, be sure to list all appendices and a references section in your table of contents. Include page numbers for these items but do not assign separate chapter numbers.
• Entries must align with the document's left margin or be indented to the right of the left page margin using consistent tabs.
• Major subheadings within chapters must be included in the table of contents. The subheading(s) should be indented to the right of the left page margin using consistent tabs.
• If an entry takes up more than one line, break up the entry about three-fourths of the way across the page and place the rest of the text on a second line, single-spacing the two lines.
• Include one double-spaced line between each entry.
• Page numbers listed in the table of contents must be located just inside the right page margin with leaders (lines of periods) filling out the space between the end of the entry and the page number. The last digit of each number must line up on the right margin.
• Information included in the table of contents must match the headings, major subheadings, and numbering used in the body of the thesis or dissertation.
• The Table of Contents page(s) must be numbered with consecutive lower case Roman numerals centered with a 1/2″ margin from the bottom edge.

Lists of Tables, Figures, and Illustrations

If applicable, include a list of tables, list of figures, and/or list of illustrations following these guidelines:

• Include the heading(s) in all capital letters, centered 1″ below the top of the page.
• Each entry must include a number, title, and page number.
• Assign each table, figure, or illustration in your thesis or dissertation an Arabic numeral. You may number consecutively throughout the entire work (e.g., Figure 1, Figure 2, etc.), or you may assign a two-part Arabic numeral with the first number designating the chapter in which it appears, separated by a period, followed by a second number to indicate its consecutive placement in the chapter (e.g., Table 3.2 is the second table in Chapter Three).
• Numerals and titles must align with the document's left margin or be indented to the right of the left page margin using consistent tabs.
• Page numbers must be located just inside the right page margin with leaders (lines of periods) filling out the space between the end of the entry and the page number. The last digit of each number must line up on the right margin.
• Numbers, titles, and page numbers must each match the corresponding numbers, titles, and page numbers appearing in the thesis or dissertation.
• All Lists of Tables, Figures, and Illustrations page(s) must be numbered with consecutive lower case Roman numerals centered with a 1/2″ margin from the bottom edge.

If you use abbreviations extensively in your thesis or dissertation, you must include a list of abbreviations and their corresponding definitions following these guidelines:

• Include the heading “LIST OF ABBREVIATIONS” in all capital letters, and center it 1″ below the top of the page.
• Arrange your abbreviations alphabetically.
• Abbreviations must align with the document's left margin or be indented to the right of the left page margin using consistent tabs.
• If an entry takes up more than one line, single-space between the two lines.
• The List of Abbreviations page(s) must be numbered with consecutive lower case Roman numerals centered with a 1/2″ margin from the bottom edge.

If you use symbols in your thesis or dissertation, you may combine them with your abbreviations, titling the section “LIST OF ABBREVIATIONS AND SYMBOLS”, or you may set up a separate list of symbols and their definitions by following the formatting instructions above for abbreviations. The heading you choose must be in all capital letters and centered 1″ below the top of the page.

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What Is a Good H-Index Required for an Academic Position?

Posted by Rene Tetzner | Sep 3, 2021 | Career Advice for Academics , How To Get Published | 0 |

What Is a Good H-Index Required for an Academic Position? Metrics are important. Even scholars who may not entirely agree with the ways in which academic and scientific impact is currently measured and used cannot deny that metrics play a significant role in determining who receives research grants, employment offers and desirable promotions. The h-index is only one among various kinds of metrics now applied to the research-based writing of professional scholars, but it is an increasingly significant one. Introduced by the physicist Jorge Hirsch in a paper published in 2005, the h-index was designed to assess the quantity and quality of a scientist’s contributions and predict his or her productivity and influence in the coming years. However, its use and importance have quickly expanded beyond physics and the sciences into a wide variety of disciplines and fields of study. If you are applying for a scientific or academic position, hoping for a promotion or in need of research funding, it will therefore be wise to give your h-index score some consideration, but within reason. In some fields, the h-index and other forms of metrics play a very small part if any in hiring and funding, and there are still many other means used by hiring and funding committees to assess scholarly contributions.

The h-index is considered preferable to metrics that measure only a researcher’s number of publications or the number of times those publications have been cited. This is because it combines the two, considering both publications and citations to arrive at a particular value. A scholar who has five publications that have been cited at least five times has an h-index of 5, whereas a scholar with ten publications that have been cited ten times has an h-index of 10. Publication and citation patterns differ markedly across disciplines and fields of study, and the expectations of hiring and funding bodies vary depending on the level and type of position and the kind and size of research project, so it is impossible to say exactly what might be considered an acceptable or competitive h-index in a given situation. H-index scores between 3 and 5 seem common for new assistant professors, scores between 8 and 12 fairly standard for promotion to the position of tenured associate professor, and scores between 15 and 20 about right for becoming a full professor. Be aware, however, that these are gross generalisations and actual figures vary enormously among disciplines and fields: there are, for instance, many full professors, deans and chancellors with very low h-index scores, and an exceptional young researcher with an h-index of 10 or 15 might conceivably still be working on a post doctorate.

As a general rule in many fields, an h-index that matches the number of years a scholar has been working in the field is a respectable score. Hirsch in fact suggested that the h-index be used in conjunction with a scholar’s active research time to arrive at what is known as Hirsch’s individual m. It is calculated by dividing a scientist’s h-index by the number of years that have passed since the first publication, with a score of 1 being very good indeed, 2 being outstanding and 3 truly exceptional. This means that if you have published at least one well-cited document each year since your first publication – a decent textual output by any measure – you are among a successful group of scholars, and if you have published two or three times that number of well-cited documents over the same period of time, you are among the intellectual superstars of your discipline and probably of your time. To put this into perspective, from what I can find online it looks like Stephen Hawking has a score of about 1.6 by this calculation. If you can approach a hiring committee or funding body with anything close to that, you are certainly going to be a serious contender in the competition.

The h-index as a measure of both the quantity and quality of scholarly achievement is considered quite reliable and robust, so it has proved incredibly popular and is now applied not only to individual researchers, but also to research groups and projects, to scholarly journals and publishers, to academic and scientific departments, to entire universities and even to entire countries. As with all metrics, however, the h-index is subject to a number of biases and limitations, so there are significant problems associated with relying solely on h-index scores when making important research and career decisions. The h-index does not, for example, account for publications with citation numbers far above a researcher’s h-index or distinguish any difference between publications with a single author or many. Older publications are counted exactly as more recent ones are and older scholars benefit, whether they have published anything new in years or not. Neither the length of a publication nor the nature of each citation (positive or negative) is considered, so those measures of quantity and quality are not part of the picture. Early career researchers who take the time to delve deeply into an important problem and eventually produce an excellent article and scholars at any stage in their careers who dedicate time to teaching or practical applications of research will have lower scores than those who crank out mediocre articles based on uninteresting research that is nonetheless cited by their colleagues. Finally, the databases from which the h-index and other metrics are determined vary in the types of documents they consider and the fields of study they include, so the same scholar will not receive the same h value across all of them, and accurate comparison across fields and disciplines is impossible.

These and other problems have generated a number of adjustments that are rather similar to Hirsch’s individual m, which, as discussed above, considers a scholar’s active research time in relation to his or her h-index. The g-index gives greater weight to publications whose citation counts exceed a researcher’s h value; the hi index corrects for the number of authors; the hc index corrects for the age of publications, with recent citations earning more counts; and the c-index considers collaboration distance between the author of a publication and the authors citing it. Solutions for comparison between disciplines and fields have included dividing the h-index scores of scholars by the h-index averages in their respective fields to arrive at results that can be compared, but defining fields can be tricky, and larger fields of study with more researchers naturally generate more citations. The databases used for scholarly metrics are constantly upgrading and broadening their inclusiveness to render metrics like the h-index more truly representative of a researcher’s actual productivity and impact, so the accuracy and consistency of these tools are likely to continue improving. However, no new numbers or calculations can add what all of these metrics lack, and that is research content – the valuable and unique content that makes the publication of research a worthy task in the first place.

Committees gathered to hire or promote faculty or to select the recipients of research grants rarely rely solely on metrics when making their decisions. If they are doing their jobs properly, they combine what they can gather from metrics with other information about candidates and their scholarly impact. They do not just notice how many times the papers of candidates have been cited; they read those papers and consider their content, and they pay attention to the other activities of the scholars they are considering. This wider perspective is appropriate for an applicant as well, so if you are polishing your CV, putting together a grant application or preparing for a job interview, look over your own unique achievements with a kindly yet critical eye and consider them in direct relation to what the job posting or grant regulations indicate is wanted. If you happen to have a wonderful h-index score or any other impressive metrics, by all means flaunt them, and if you fear that a low h value will compromise your career aspirations, do what you can to have your publications with lower citation counts read and used more often, update your profiles on the relevant databases, and publish the type of document sure to garner citations in your field, such as a review article.

Do keep in mind, however, that hiring and funding committees are often looking for far more than large numbers of highly cited publications. Admittedly, they rarely balk at them, but universities are also seeking excellent teachers, advisors and administrators, so play up those skills and any related experience you have, and remember that financial supporters of research may be keen to fund scholars who can successfully manage and complete projects, even and perhaps especially if part of the training they offer younger researchers means that their students tend to publish most of the results. Finally, an active online presence in your field established through sharing your research via blogs, professional platforms and social media might not garner the same respect as formal publications, but it can count for a great deal when many universities are working to increase their online activities and funding bodies working to democratise the publication of the research they support. Generally speaking, committees considering applications will be even more likely to google the names of candidates and applicants than to look up the metrics associated with them, so assume that both will be done and ensure that what can be found shares excellent research content and leaves a desirable professional impression of you and your work.

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What Is a Good H-Index Required for an Academic Position? The h-index is used along with applicants research & skills to measure their impact

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What is the H-index, and Does it Matter?

How do you measure how good you are as a scientist? One way is the h-index. Discover what this is, and learn about the pros and cons of using it to assess your scientific career.

Published October 20, 2023

Nick has a PhD from the University Dundee and is the Founder and Director of Bitesize Bio , Science Squared Ltd and The Life Science Marketing Society .

The h-index is a measure of research performance and is calculated as the highest number of manuscripts from an author (h) that all have at least the same number (h) of citations. The h-index is known to penalize early career researchers and does not take into account the number of authors on a paper. Alternative indexes have been created, including the i-10, h-frac, G-index, and M-number.

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How do you measure how good you are as a scientist? How would you compare the impact of two scientists in a field? What if you had to decide which one would get a grant? One method is the h-index, which we will discuss in more detail below. First, we’ll touch on why this is not a simple task.

Measuring scientific performance is more complicated and more critical than it might first seem. Various methods for measurement and comparison have been proposed, but none of them is perfect.

At first, you might think that the method for measuring scientific performance doesn’t concern you—because all you care about is doing the best research you can. However, you should care because these metrics are increasingly used by funding bodies and employers to allocate grants and jobs. So, your perceived scientific performance score could seriously affect your career.

Metrics for Measuring Scientific Performance

What are the metrics involved in measuring scientific performance? The methods that might first spring to mind are:

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• Recommendations from peers. At first glance, this is a good idea in principle. However, it is subject to human nature, so personal relationships will inevitably affect perceived performance. Also, if a lesser-known scientist publishes a ground-breaking paper, they would likely get less recognition than if a more eminent colleague published the same paper.
• The number of articles published. A long publication list looks good on your CV, but the number of articles published does not indicate their impact on the field. Having a few publications well-heeded by colleagues in the field (i.e., they are cited often) is better than having a long list of publications cited poorly or not at all.
• The average number of citations per article published. So, if it’s citations we’re interested in, then surely the average number of citations per paper is a better number to look at. Well, not really. The average could be skewed dramatically by one highly cited article, so it does not allow a good comparison of overall performance.

The H-Index

In 2005, Jorge E. Hirsch of UCSD published a paper in PNAS in which he put forward the h-index as a metric for measuring and comparing the overall scientific productivity of individual scientists. [1]

The h-index has been quickly adopted as the metric of choice for many committees and bodies.

How to Calculate An Author’s H-Index

The h-index calculation is pretty simple. You plot the number of papers versus the number of citations you (or someone else) have received, and the h-index is the number of papers at which the 45-degree line (citations=papers, orange) intercepts the curve, as shown in Figure 1 . That is, h equals the number of papers that have received at least h citations. For example, do you have one publication that has been cited at least once? If the answer is yes, then you can go on to your next publication. Have your two publications each been cited at least twice? If yes, then your h-index is at least 2. You can keep going until you get to a “no.”

So, if you have an h-index of 20, you have 20 papers with at least 20 citations. It also means that you are doing pretty well with your science!

What is a Good H-Index?

Hirsch reckons that after 20 years of research, an h-index of 20 is good, 40 is outstanding, and 60 is truly exceptional.

In his paper, Hirsch shows that successful scientists do, indeed, have high h-indices: 84% of Nobel Prize winners in physics, for example, had an h-index of at least 30. Table 1 lists some eminent scientists and their respective h-indexes.

Table 1: H-index scores of some Nobel Laureates (data from Google Scholar collected on September 27, 2023).

Advantages of the H-Index

The advantage of the h-index is that it combines productivity (i.e., number of papers produced) and impact (number of citations) in a single number. So, both productivity and impact are required for a high h-index; neither a few highly cited papers nor a long list of papers with only a handful of (or no!) citations will yield a high h-index.

Limitations of the H-Index

Although having a single number that measures scientific performance is attractive, the h-index is only a rough indicator of scientific performance and should only be considered as such.

Limitations of the h-index include the following:

• It does not take into account the number of authors on a paper. A scientist who is the sole author of a paper with 100 citations should get more credit than one on a similarly cited paper with 10 co-authors.
• It penalizes early-career scientists. Outstanding scientists with only a few publications cannot have a high h-index, even if all of those publications are ground-breaking and highly cited. For example, Albert Einstein would have had an h-index of only 4 or 5 if he had died in early 1906 despite being widely known as an influential physicist at the time.
• Review articles have a greater impact on the h-index than original papers since they are generally cited more often.
• The use of the h-index has now broadened beyond science. However, it’s difficult to compare fields and scientific disciplines directly, so, really, a ‘good’ h-index is impossible to define.

Calculating the H-Index

There are several online resources and h-index calculators for obtaining a scientist’s h-index. The most established are ISI Web of Knowledge, and Scopus, both of which require a subscription (probably via your institution), but there are free options too, one of which is Publish or Perish .

You might get a different value if you check your own (or someone else’s) h-index with each of these resources. Each uses a different database to count the total publications and citations. ISI and Scopus use their own databases, and Publish or Perish uses Google Scholar. Each database has different coverage and will provide varying h-index values. For example, ISI has good coverage of journal publications but poor coverage of conferences, while Scopus covers conferences better but needs better journal coverage pre-1992. [2]

Is the H-index Still Effective?

A paper published in PLoS One in 2021 concluded that while a scientist’s h-index previously correlated well with the number of scientific awards, this is no longer the case. This lack of correlation is partly because of the change in authorship patterns, with the average number of authors per paper increasing. [3]

Are Alternatives to the H-Index Better?

Let’s take a look at some of the alternative measures available.

The H-Frac Index

The authors of the PLoS One paper suggest fractional analogs of the h-index are better suited for the job. [3] Here, the number of authors on a paper is also considered. One such measure is the h-frac, where citation counts are divided by the number of authors. However, this solution could also be manipulated to the detriment of more junior researchers, as minimizing the number of authors on a paper would maximize your h-frac score. This could mean more junior researchers are left off papers where they did contribute, harming their careers. 

The G-Index

This measure looks at the most highly cited articles of an author and is defined as “the largest number n of highly cited articles for which the average number of citations is at least n .” [4] This measure allows highly cited papers to bolster lower cited papers of an author. 

The i-10 Index

Developed by Google Scholar, this index is the number of articles published by an author that have received at least 10 citations. This measure, along with the h-index, is available on Google Scholar.

The m-value was developed to try to balance the scales for early career researchers. It corrects the h-index for time, allowing for easier comparison of researchers with different seniority and career lengths. It is calculated as the h-index divided by the number of years.

The Problem with Measuring Performance

While these numbers can be helpful to give a flavor of a scientist’s performance, they are all flawed. Many are biased towards researchers who publish often and are further into their careers. Many of these indexes can also be manipulated, such as adding extra authors to papers who didn’t contribute.

In reality, it isn’t possible to distill a researcher’s contributions to a single number. They may not have published many papers, but those papers they have published made vital contributions. Or their skills are in training the next round of researchers. When looking at these numbers, we should remember they are just a reflection of one small part of a researcher’s contributions and values and are not the be-all and end-all.

The H-Index Summed Up

The h-index provides a useful metric for scientific performance, but only when viewed in the context of other factors. While other measures are available, including the i-10 index, the G-index, and the h-frac index, these also have limitations. Therefore, when making decisions that are important to you (funding, job, finding a PI), be sure to read through publication lists, talk to other scientists (and students) and peers, and take account of career stage. So, remember that an h-index is only one consideration among many—and you should definitely know your h-index—but it doesn’t define you (or anyone else) as a scientist.

• Hirsch JE. (2005) An index to quantify an individual’s scientific research output . PNAS 102(46):16569–72
• Meho LI, Yang K. (2007) Impact of data sources on citation counts and rankings of LIS faculty: Web of science versus scopus and google scholar . JASIST 58(13):2105–25
• Koltun V, Hafner D. (2021) The h-index is no longer an effective correlate of scientific reputation . PLoS One . 16(6):e0253397
• Wikipedia. g-index . Accessed 25 September 2023

Originally published April 2, 2009. Reviewed and updated October 2023.

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h-index for journals

H-index for institutions, computing your own h-index, ways to increase your h-index, limitations of the h-index, variations of the h-index.

• Using Scopus to find a researcher's h-index
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h-index for researchers-definition

• The h-index is a measure used to indicate the impact and productivity of a researcher based on how often his/her publications have been cited.
• The physicist, Jorge E. Hirsch, provides the following definition for the h-index:  A scientist has index h if  h of his/her N p  papers have at least h citations each, and the other (N p  − h) papers have no more than h citations each. (Hirsch, JE (15 November 2005) PNAS 102 (46) 16569-16572)
• The h -index is based on the highest number of papers written by the author that have had at least the same number of citations.
• A researcher with an h-index of 6 has published six papers that have been cited at least six times by other scholars.  This researcher may have published more than six papers, but only six of them have been cited six or more times. 

Whether or not a h-index is considered strong, weak or average depends on the researcher's field of study and how long they have been active.  The h-index of an individual should be considered in the context of the h-indices of equivalent researchers in the same field of study.

Definition :  The h-index of a publication is the largest number h such that at least h articles in that publication were cited at least h times each. For example, a journal with a h-index of 20 has published 20 articles that have been cited 20 or more times.

Available from:

• SJR (Scimago Journal & Country Rank)

Whether or not a h-index is considered strong, weak or average depends on the discipline the journal covers and how long it has published. The h-index of a journal should be considered in the context of the h-indices of other journals in similar disciplines.

Definition :  The h-index of an institution is the largest number h such that at least h articles published by researchers at the institution were cited at least h times each. For example, if an institution has a h-index of 200 it's researchers have published 200 articles that have been cited 200 or more times.

Available from: exaly

In a spreadsheet, list the number of times each of your publications has been cited by other scholars. 

Sort the spreadsheet in descending order by the number of  times each publication is cited.  Then start counting down until the article number is equal to or not greater than the times cited.

Article                   Times Cited

1                              50          

2                              15          

3                              12

4                              10

5                              8

6                              7              == =>h index is 6

7                              5             

8                              1

How to successfully boost your h-index (enago academy, 2019)

Glänzel, Wolfgang On the Opportunities and Limitations of the H-index. , 2006

• h -index based upon data from the last 5 years
•  i-10 index is the number of articles by an author that have at least ten citations. 
•  i-10 index was created by Google Scholar .
• Used to compare researchers with different lengths of publication history
• m-index =   ­­­­­­­­­­­­­­­­­­___________ h-index _______________                      # of years since author’s 1 st publication

Using Scopus to find an researcher's h-index

Additional resources for finding a researcher's h-index.

Web of Science Core Collection or Web of Science All Databases

• Perform an author search
• Create a citation report for that author.
• The h-index will be listed in the report.

Set up your author profile in the following three resources.  Each resource will compute your h-index.  Your h-index may vary since each of these sites collects data from different resources.

• Google Scholar Citations Computes h-index based on publications and cited references in Google Scholar .
• Researcher ID
• Computes h-index based on publications and cited references in the last 20 years of Web of Science .
• << Previous: Researcher Profiles
• Next: Journal Impact Factor & other journal rankings >>
• Last Updated: Jul 8, 2024 3:20 PM
• URL: https://libraryguides.missouri.edu/researchidentity

Research guidance, Research Journals, Top Universities

Explaining H-index, i10-index, G-index & other research metrics

This blog post aims to explain various  research metrics like the h-index, i-10 index, and g-index . Moreover, we will also be explaining how you can increase these research metrics .

Page Contents

Measuring your research impact

Researchers use different metrics to measure the quality of published papers in journals . It basically gives an idea of the impact of any research paper . These metrics can be applied to any publication on any subject across the world. Through research metrics, one can monitor and quantify published articles. These citation metrics ultimately help in getting a university’s ranking .

Research metrics are one of the most established ways to measure the quality of research work. It tells the importance of particular research. Nowadays, H-index, impact factor , G-index, i-10 index are commonly used research metrics. These metrics help in measuring how much a researcher’s article is cited by the co-researchers. It helps in increasing the impact of the research work.  Researchers can use these metrics for availing various fellowships and scholarships, and gaining job opportunities across the world. 

Also, read the following articles:

Difference between SCI, SCIE, and ESCI journals

Difference between Scopus and Web of Science (WoS)

What is the h-index?

It is commonly known as the Hirsch number or Hirsch index. It was developed by American physicist Jorge E. Hirsch in 2005. h-index can be defined as for a given value of h, the researchers should h number of published articles that are cited at least by h-times. Suppose the author has an h-index of 25, which means that each of his published articles is cited at least 25 times by other researchers. It mainly gives an idea of the quality of the research papers. Generally, the higher the h-index, the greater the impact of a research paper will be. Thus, the h-index can be used to measure the quality and quantity of the research paper simultaneously. The h-index for any author can be determined manually with the help of any citation database. Using Scopus or Web of Science data, the h-index can also be calculated.

What is the i-10 index?

It is another commonly used research metric by the authors/researchers. i-10 index is provided by Google Scholar . It can define as a measure of having publications with at least 10 citations. For example, if an author/researcher’s i-10 index is 6, it indicates that six of his/her publications are cited 10 times. i-10 index also helps in increasing the weightage of any student profile. The main advantage of the i-10 index is that it can be calculated very easily. Google Scholar provides easy and free access to find out these metrics. 

Charles Robert Darwin, a renowned scientist, has the highest number of citations to date. This scientist has 156678 citations with an h-index of 106 and an i-10 index of 526. This means this researcher has received at least 10 citations for each of the 526 published articles. An h-index of 106 means that, out of his total publications, his 106 articles have been cited at least 106 times by different researchers.

What is G-index?

It is another level of measuring research metrics. It was suggested by Leo Egge in 2006. In general, the h-index does include a citation count of highly cites papers. But g-index helps in boosting the profile of a researcher by giving preference to highly cited papers. G-index is basically an advanced version of the h-index.  G-index measures the citation performance for a set of articles. A g-index of 20 indicates that the top 20 publications in a researcher/author profile are cited by 400 times (20 2 ). Similarly, a g-index of 10 indicates that the top 10 publications in a researcher profile are cited by 100 times (10 2) . 

How to increase the h-index? 

In the present scenario, the quality of any published article is measured by the number of citations he/she received, research metrics like the impact factor of the journal he/she has published, and the h-index of any author profile. Generally, during the entire research career, if the researcher receives of h-index of 25 or more, it is considered to be an excellent researcher’s profile. However, on average most of the researchers have an h-index between 10-15.

• In order to increase the h-index, one must publish papers of high quality. The researcher should ensure that he/she has not published any article in predatory/fake journals . The researcher should publish more and more original research articles . Although, sometimes publishing more review articles receives a greater number of citations , that ultimately increases the h-index in a profile.  
• Secondly, another way of increasing the h-index is through proper communication of the published article. He/she can advertise through various social media platforms such as Twitter , and ResearchGate, and continuously update the Google scholar profile. This will mainly help in increasing the visibility of published articles. 
• Thirdly, the researchers while writing the manuscript , he/she should ensure that the title of the paper is simple, clear, short, and concise. He/she should use a maximum of 5-6 appropriate keywords in the abstract. The abstract should be written in a very informative manner. It should briefly describe the research study. The research paper should always explain the novelty/newness of his/her article. Usually, the first sentence of the article appears in the all-search engines. So, it should be written in a very attractive manner. The abstract should be written in a such way it gives an overall summary of the research findings. 
• Fourthly, if it is possible, the researcher should publish in open-access journals . OA journals also undergo a peer-review process. Generally, these journals are available on online platforms which are easy to access and free of charge. Through open-access journals, readers can get full-text access to published articles easily. It will ultimately draw the attention of more audiences, which will ultimately help in gaining citations, thus increasing the h-index. 

What is considered to be a good i-10 index? 

Similar to the h-index, if the author/researcher has an i-10 index of 25 or more, it is considered an excellent research profile. An i-10 index of 25 means that, out of total publications, the researcher has received at least 10 citations for every 25 published articles. The i-10 index differs from researcher to researcher. It mainly depends on the subject area and sub-section of the research area. Generally, publishing more articles related to solving practical problems receives a greater number of citations. Professors with arts and humanities backgrounds may not have a higher i-index as compared to professors with science backgrounds. However, the i-10 index is the second-well-recognized research metric after the h-index.

I Hope, this blog post will help you to understand various research metrics used in research.

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• Knowledge Base
• Dissertation
• Dissertation Table of Contents in Word | Instructions & Examples

Dissertation Table of Contents in Word | Instructions & Examples

Published on May 15, 2022 by Tegan George . Revised on July 18, 2023.

The table of contents is where you list the chapters and major sections of your thesis, dissertation , or research paper, alongside their page numbers. A clear and well-formatted table of contents is essential, as it demonstrates to your reader that a quality paper will follow.

The table of contents (TOC) should be placed between the abstract and the introduction . The maximum length should be two pages. Depending on the nature of your thesis , paper, or dissertation topic , there are a few formatting options you can choose from.

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Table of contents

What to include in your table of contents, what not to include in your table of contents, creating a table of contents in microsoft word, table of contents examples, updating a table of contents in microsoft word, other lists in your thesis, dissertation, or research paper, other interesting articles, frequently asked questions about the table of contents.

Depending on the length of your document, you can choose between a single-level, subdivided, or multi-level table of contents.

• A single-level table of contents only includes “level 1” headings , or chapters. This is the simplest option, but it may be too broad for a long document like a dissertation.
• A subdivided table of contents includes chapters as well as “level 2” headings, or sections. These show your reader what each chapter contains.
• A multi-level table of contents also further divides sections into “level 3” headings. This option can get messy quickly, so proceed with caution. Remember your table of contents should not be longer than 2 pages. A multi-level table is often a good choice for a shorter document like a research paper .

Examples of level 1 headings are Introduction, Literature Review , Methodology , and Bibliography. Subsections of each of these would be level 2 headings, further describing the contents of each chapter or large section. Any further subsections would be level 3.

In these introductory sections, less is often more. As you decide which sections to include, narrow it down to only the most essential.

Including appendices and tables

You should include all appendices in your table of contents. Whether or not you include tables and figures depends largely on how many there are in your document.

If there are more than three figures and tables, you might consider listing them on a separate page. Otherwise, you can include each one in the table of contents.

• Theses and dissertations often have a separate list of figures and tables.
• Research papers generally don’t have a separate list of figures and tables.

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All level 1 and level 2 headings should be included in your table of contents, with level 3 headings used very sparingly.

The following things should never be included in a table of contents:

• Your acknowledgements page
• Your abstract
• The table of contents itself

The acknowledgements and abstract always precede the table of contents, so there’s no need to include them. This goes for any sections that precede the table of contents.

To automatically insert a table of contents in Microsoft Word, be sure to first apply the correct heading styles throughout the document, as shown below.

• Choose which headings are heading 1 and which are heading 2 (or 3)!
• For example, if all level 1 headings should be Times New Roman, 12-point font, and bold, add this formatting to the first level 1 heading.
• Highlight the level 1 heading.
• Right-click the style that says “Heading 1.”
• Select “Update Heading 1 to Match Selection.”
• Allocate the formatting for each heading throughout your document by highlighting the heading in question and clicking the style you wish to apply.

Once that’s all set, follow these steps:

• Add a title to your table of contents. Be sure to check if your citation style or university has guidelines for this.
• Place your cursor where you would like your table of contents to go.
• In the “References” section at the top, locate the Table of Contents group.
• Here, you can select which levels of headings you would like to include. You can also make manual adjustments to each level by clicking the Modify button.
• When you are ready to insert the table of contents, click “OK” and it will be automatically generated, as shown below.

The key features of a table of contents are:

• Clear headings and subheadings
• Corresponding page numbers

Check with your educational institution to see if they have any specific formatting or design requirements.

Write yourself a reminder to update your table of contents as one of your final tasks before submitting your dissertation or paper. It’s normal for your text to shift a bit as you input your final edits, and it’s crucial that your page numbers correspond correctly.

It’s easy to update your page numbers automatically in Microsoft Word. Simply right-click the table of contents and select “Update Field.” You can choose either to update page numbers only or to update all information in your table of contents.

In addition to a table of contents, you might also want to include a list of figures and tables, a list of abbreviations, and a glossary in your thesis or dissertation. You can use the following guides to do so:

• List of figures and tables
• List of abbreviations

It is less common to include these lists in a research paper.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract in the table of contents.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction .

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Accredited freelance professional indexer

Indexing: a guide for academic authors

Congratulations: your proposal has been accepted and you’ve done the hard work needed to write your book.  Before it’s published, however, your book is going to need an index.

Anyone who’s done a research degree knows how important a useful index can be – and how frustrating a poor one is.  A good index provides a map of your book, with multiple access points for readers with different needs and interests.  It will make your book – and your research – more accessible to readers, and can even increase sales, especially to libraries. For more on the value a professional index will add to your book, see indexer Melanie Gee’s blog post .

In this short guide I will explain when and how indexing takes place, discuss the advantages of working with a professional indexer, and provide some useful links to information that can help you through the indexing process.  You may also want to read fellow indexer Paula Clarke Bain’s excellent guide to the process for the general author .

1: Indexing in the publishing process

Indexing is carried out during the later stages of the publication schedule.  Your publisher should provide you with an indicative schedule to explain when indexing should take place.  The precise timing depends on the type of index required, and there are three main possibilities:

• traditional back-of-book index, complied from the PDF proofs of your book: these indexes are normally created at the very end of the publishing process, at the same time as proofreading. Copy-editing and formatting, and any changes required during that process, will all have been completed; any figures or illustrations will be in place and the page numbers will be fixed.  A fast turnaround is often required, with indexing and proofreading to be completed within a month or less.
• embedded index, compiled by adding codes to a Word document which can be used to generate the index. This is normally done after copy-editing but before typesetting; the codes in Word are used by the typesetter to generate the index once the final layout is confirmed.  Embedded indexing can save time in the publication process and is particularly useful for books that may need to be published in revised editions, or which will be simultaneously published as ebooks.  You may get a slightly longer period of time to complete an embedded index.
• indexing to paragraph numbers for future embedding.  In this system, the index is compiled using paragraph numbers from early-stage proofs, rather than page numbers.  Again, this is done earlier in the publication process after copy-editing but before typesetting.  The index will be embedded into the text during typesetting, and the final index will show the page numbers automatically. In some subject areas, indexing to paragraph numbers is standard, but is done from final page proofs, and the paragraph numbers are given in the index rather than page numbers.

Some publishers will still fund the index themselves, or give you the option to have the cost deducted from royalties. In this case, your editor will arrange for an indexer to work on your book.  You are likely to be asked to review the index; this is covered in section 4 below.  However, increasingly, publishers ask the authors of monographs (and volume editors, for edited collections) to organise indexing themselves.  If you are asked to do this, you have the option of working with a professional indexer, or creating the index yourself.

Although there are automated systems that can help with the indexing process, automated indexes generated from document searches are not really indexes at all – they don’t include the vital component of human analysis that decides whether a topic should be in the index, and how to represent it.  Searching cannot identify implicit mentions – a book I indexed recently, for example, discussed various wars without using the standard historical name for each one, and I needed to check the correct names for use in the index.  Similarly, searching cannot easily identify synonyms or distinguish between homonyms.   Searching will also bring up all references, including repetitions and passing mentions which do not include any meaningful information about the term or concepts.

2: Working with a professional indexer

Professional indexers will read your book carefully, identify the indexable terms and concepts, and create an index that anticipates the needs of your readers so that they can access your ideas and research quickly and easily.  We use specialist software to organise the index, to ensure it is consistent and that it meets the presentation requirements of your publisher. We have a wide range of subject expertise and are often very highly qualified.

In the UK, the Society of Indexers has a directory of members that you can search by subject area and type of index: see https://www.indexers.org.uk/find-an-indexer/directory/ .  If your book is being published by a US publisher, you may want to use a US indexer.  See the American Society for Indexing: https://www.asindexing.org/find-an-indexer/asi-indexer-locator/ .  There are also professional indexing societies in Ireland  ( https://www.afepi-ireland.com/ ), Canada ( http://indexers.ca/ ),  Australia and New Zealand ( https://www.anzsi.org/ ),  Germany ( http://www.d-indexer.org/welcome.html ),  Netherlands ( https://www.indexers.nl/ ) and South Africa ( https://www.asaib.org.za/ )

As well as these sources, you could consult colleagues for recommendations, or find out who has recently indexed similar books in your subject area from the indexing societies’ professional directories.  If you need to submit an embedded index, make sure you approach indexers who have experience of this way of working – the Society of Indexers Directory allows you to search for this particular skill. Do get several quotes, and don’t be shy about asking for references from previous clients.

When you’ve found some likely candidates to work on your book, it’s best to contact them as soon as you have an indicative schedule.  We do get booked up with work and it can be hard to accommodate last-minute requests.  We are used to dealing with slipping schedules, however, so don’t worry if there are delays to your book beyond your control.  When you’ve chosen your indexer, let any others who quoted for the work know that you won’t be using them.

How much will it cost?  The UK Society of Indexers suggests recommended minimum rates: in 2024, these are £3.55 per page or £9.55 per 1000 words .  A 60,000 word monograph, for example, would cost around £570 to index at these rates.  More advanced or experienced indexers may charge a higher fee, and you might be quoted a higher fee for a last-minute job, for a particularly complex text, or for an embedded index.  Indexing can seem expensive, but consider the time and stress you will save and the eventual quality of your index.

If you are working at a university, it’s worth checking whether there is any funding available to help with the cost of indexing.  I’ve indexed many books for academic authors, including early career researchers, where the cost has been covered by their institution.  Most indexers will invoice you after completion, although you may be asked to pay part of the fee in advance. If you are bidding for funding for a project that will result in a book, remember to include the cost of indexing in your bid – then you’ll have the funds to pay for it when your project is concluding. And if you have budget to use up before the indexing work needs to be done, you can ask your indexer to invoice in advance.

Embedded indexing in Word is particularly fiddly and time-consuming.  Indexers use specialist add-ons to connect Word to our indexing software to do this work, which does make it easier.  As an indexer, I would obviously recommend using a professional for any index – but especially if you have to produce an embedded index.

3: Going it alone

If you decide to index your book yourself, there are a number of sources of help and guidance out there.  Give yourself plenty of time, especially towards the end of your schedule when you’ll need to check and edit your index.  Based on the UK recommended hourly rate, a professional indexer would take around 18 hours to index a 60,000 word monograph; I was a lot slower than that when I started out and would still be much slower without indexing software.  I’d expect a beginner, without the benefit of specialist software, to take about twice as long. Never index when you’re tired, and remember that most of us can only do about 4 hours of intense work a day – and indexing is definitely intense work.  Paula Clarke Bain has written an excellent account of the indexing process for CIEP, which gives helpful insight into how indexing is done.

It’s better and easier to start at the beginning and work through your book, rather than making a list of keywords and searching for them.  The keyword approach can seem quicker, but you may miss implicit discussion of key topics and be tempted to add passing mentions; it’s also a very boring task. A mind map of the book’s main topics is often helpful, though. You may find it useful to edit after each chapter, and remember that it’s easier to take something out than to go back and put it in. Follow your publisher’s guidelines to the letter, especially in relation to the number of pages available for the index.  If you send back an index that’s too long, they’ll probably just ask you to reduce it.

As well as your publisher’s guidelines, you might want to look at the following books and short courses (most of the online courses come at a cost, your library may well stock the books):

• Nancy C. Mulvaney’s Indexing Books (Chicago and London: University of Chicago Press, 2005, 2nd edition) is an accessible and thorough guide to the indexing process
• The Society of Indexers offers an online Indexing Basics workshop, which you can take at any time (study time 6-7 hours): https://www.indexers.org.uk/training-development/workshops/online-workshops/indexing-basics/
• Indexer Stephen Ullstrom has written Book Indexing: a Step-by-Step Guide , aimed at authors who want to write the
• US indexer Sylvia Coates runs this online course: https://www.canvas.net/browse/canvasnet/courses/indexing-books
• Alex Gazzola at Mistakes Writers Make gives the author’s perspective on index writing: https://mistakeswritersmake.com/should-you-compile-your-own-index/

One of the risks of going it alone is that you may find that you really enjoy indexing and want to do more of it.  The indexing societies listed above can all provide advice on further training and accreditation, if you discover you’re an indexer at heart.

4: Reviewing the index

Whoever writes your index, as the author you will need to review it.  When you receive it, you should consider the following questions:

• Are all the main concepts and topics represented in the index?
• Are the words used for the index entries clear? Will they make sense to my readers?
• Are there long strings of page references, or long page spans, that have not been broken up with appropriate subheadings?
• Do the cross-references make sense?
• Are the page numbers accurate? It’s worth spot-checking to make sure.

Further guidance on index reviewing is available:

• Society of Indexers Fellow Melanie Gee provides a very useful four-point plan in her blog post on how to evaluate an index
• Ideas on Fire give a good overview of the review process, tips on what not to tinker with, and how to feed back to your professional indexer  https://ideasonfire.net/reviewing-your-index-draft/
• Helen Kara explains in detail how to check an index at https://helenkara.com/2019/04/11/how-to-check-an-index/ ,
• Stephen Ingle at the Textbook and Academic Authors Association offers a list of 10 tips on index evaluation
• detailed guidance on index evaluation is available from the American Society of Indexers, but bear in mind that UK and US indexing practices occasionally differ: https://www.asindexing.org/about-indexing/index-evaluation-checklist/

If you are unsure about any aspect of your index, and you’ve used a professional, do contact them about it.  We’re best placed to resolve the problem quickly and will be happy to help.

However you decide to deal with your index, good luck with the indexing and with your book.

2 thoughts on “Indexing: a guide for academic authors”

How much does it cost to prepare an index per hour; I have 33,000 manuscript – should I ask for a flat rate or per hour

Most indexers will charge a flat rate for a project, rather than an hourly rate, but you can always ask your preferred indexer whether they can quote an hourly rate for you.

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Do researchers know what the h-index is? And how do they estimate its importance?

• Open access
• Published: 26 April 2021
• Volume 126 , pages 5489–5508, ( 2021 )

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• Pantea Kamrani   ORCID: orcid.org/0000-0002-8880-8105 1 ,
• Isabelle Dorsch   ORCID: orcid.org/0000-0001-7391-5189 1 &
• Wolfgang G. Stock   ORCID: orcid.org/0000-0003-2697-3225 1 , 2  

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The h-index is a widely used scientometric indicator on the researcher level working with a simple combination of publication and citation counts. In this article, we pursue two goals, namely the collection of empirical data about researchers’ personal estimations of the importance of the h-index for themselves as well as for their academic disciplines, and on the researchers’ concrete knowledge on the h-index and the way of its calculation. We worked with an online survey (including a knowledge test on the calculation of the h-index), which was finished by 1081 German university professors. We distinguished between the results for all participants, and, additionally, the results by gender, generation, and field of knowledge. We found a clear binary division between the academic knowledge fields: For the sciences and medicine the h-index is important for the researchers themselves and for their disciplines, while for the humanities and social sciences, economics, and law the h-index is considerably less important. Two fifths of the professors do not know details on the h-index or wrongly deem to know what the h-index is and failed our test. The researchers’ knowledge on the h-index is much smaller in the academic branches of the humanities and the social sciences. As the h-index is important for many researchers and as not all researchers are very knowledgeable about this author-specific indicator, it seems to be necessary to make researchers more aware of scholarly metrics literacy.

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Introduction

In 2005, Hirsch introduced his famous h-index. It combines two important measures of scientometrics, namely the publication count of a researcher (as an indicator for his or her research productivity) and the citation count of those publications (as an indicator for his or her research impact). Hirsch ( 2005 , p. 1569) defines, “A scientist has index h if h of his or her N p papers have at least h citations each and the other ( N p   –   h ) papers have <  h citations each.” If a researcher has written 100 articles, for instance, 20 of these having been cited at least 20 times and the other 80 less than that, then the researcher’s h-index will be 20 (Stock and Stock 2013 , p. 382). Following Hirsch, the h-index “gives an estimate of the importance, significance, and broad impact of a scientist’s cumulative research contribution” (Hirsch 2005 , p. 16,572). Hirsch ( 2007 ) assumed that his h-index may predict researchers’ future achievements. Looking at this in retro-perspective, Hirsch had hoped to create an “objective measure of scientific achievement” (Hirsch 2020 , p. 4) but also starts to believe that this could be the opposite. Indeed, it became a measure of scientific achievement, however a very questionable one.

Also in 2005, Hirsch derives the m-index with the researcher’s “research age” in mind. Let the number of years after a researcher’s first publication be t p . The m-index is the quotient of the researcher’s h-index and her or his research age: m p  =  h p / t p (Hirsch 2005 , p. 16,571). An m -value of 2 would mean, for example, that a researcher has reached an h-value of 20 after 10 research years. Meanwhile, the h-index is strongly wired in our scientific system. It became one of the “standard indicators” in scientific information services and can be found on many general scientific bibliographic databases. Besides, it is used in various contexts and generated a lot of research and discussions. This indicator is used or rather misused—dependent on the way of seeing—in decisions about researchers’ career paths, e.g. as part of academics’ evaluation concerning awards, funding allocations, promotion, and tenure (Ding et al. 2020 ; Dinis-Oliveira 2019 ; Haustein and Larivière 2015 ; Kelly and Jennions 2006 ). For Jappe ( 2020 , p. 13), one of the arguments for the use of the h-index in evaluation studies is its “robustness with regards to incomplete publication and citation data.” Contrary, the index is well-known for its inconsistencies, incapability for comparisons between researchers with different career stages, and missing field normalization (Costas and Bordons 2007 ; Waltman and van Eck 2012 ). There already exist various advantages and disadvantages lists on the h-index (e.g. Rousseau et al. 2018 ). And it is still questionable what the h-index underlying concept represents, due to its conflation of the two concepts’ productivity and impact resulting in one single number (Sugimoto and Larivière, 2018 ).

It is easy to identify lots of variants of the h-index concerning both, the basis of the data as well as the concrete formula of calculation. Working with the numbers of publications and their citations, there are the data based upon the leading general bibliographical information services Web of Science (WoS), Scopus, Google Scholar, and, additionally, on ResearchGate (da Silva and Dobranszki 2018 ); working with publication numbers and the number of the publications’ reads, there are data based upon Mendeley (Askeridis 2018 ). Depending of an author’s visibility on an information service (Dorsch 2017 ), we see different values for the h-indices for WoS, Scopus, and Google Scholar (Bar-Ilan 2008 ), mostly following the inequation h( R ) WoS  < h( R ) Scopus  < h( R ) Google Scholar for a given researcher R (Dorsch et al. 2018 ). Having in mind that WoS consists of many databases (Science Citation Index Expanded, Social Science Citation Index, Arts & Humanities Citation Index, Emerging Sources Citation Index, Book Citation Index, Conference Proceedings Citation Index, etc.) and that libraries not always provide access to all (and not to all years) it is no surprise that we will find different h-indices on WoS depending on the subscribed sources and years (Hu et al. 2020 ).

After Hirsch’s publication of the two initial formulas (i.e. the h-index and the time-adjusted m-index) many scientists felt required to produce similar, but only slightly mathematically modified formulas not leading to brand-new scientific insights (Alonso et al. 2009 ; Bornmann et al. 2008 ; Jan and Ahmad 2020 ), as there are high correlations between the values of the variants (Bornmann et al. 2011 ).

How do researchers estimate the importance of the h-index? Do they really know the concrete definition and its formula? In a survey for Springer Nature ( N  = 2734 authors of Springer Nature and Biomed Central), Penny ( 2016 , slide 22) found that 67% of the asked scientists use the h-index and further 22% are aware of it but have not used it before; however, there are 10% of respondents who do not know what the h-index is. Rousseau and Rousseau ( 2017 ) asked members of the International Association of Agricultural Economists and gathered 138 answers. Here, more than two-fifth of all questionees did not know what the h-index is (Rousseau and Rousseau 2017 , p. 481). Among Taiwanese researchers ( n  = 417) 28.78% self-reported to have heard about the h-index and fully understood the indicator, whereas 22.06% never heard about it. The remaining stated to hear about it and did not know its content or only some aspects (Chen and Lin 2018 ). For academics in Ireland ( n  = 19) “journal impact factor, h-index, and RG scores” are familiar concepts, but “the majority cannot tell how these metrics are calculated or what they represent” (Ma and Ladisch 2019 , p. 214). Likewise, the interviewed academics ( n  = 9) could name “more intricate metrics like h-index or Journal Impact Factor, [but] were barely able to explain correctly how these indicators are calculated” (Lemke et al. 2019 , p. 11). The knowledge about scientometric indicators in general “is quite heterogeneous among researchers,” Rousseau and Rousseau ( 2017 , p. 482) state. This is confirmed by further studies on the familiarity, perception or usage of research evaluation metrics in general (Aksnes and Rip 2009 ; Derrick and Gillespie 2013 ; Haddow and Hammarfelt 2019 ; Hammarfelt and Haddow 2018 ).

In a blog post, Tetzner ( 2019 ) speculates on concrete numbers of a “good” h-index for academic positions. Accordingly, an h-index between 3 and 5 is good for a new assistant professor, an index between 8 and 12 for a tenured associate professor, and, finally, an index of more than 15 for a full professor. However, these numbers are gross generalizations without a sound empirical foundation. As our data are from Germany, the question arises: What kinds of tools do German funders, universities, etc. use for research evaluation? Unfortunately, there are only few publications on this topic. For scientists at German universities, bibliometric indicators (including the h-index and the impact factor) are important or very important for scientific reputation for more than 55% of the questionees (Neufeld and Johann 2016 , p.136). Those indicators have also relevance or even great relevance concerning hiring on academic positions in the estimation of more than 40% of the respondents (Neufeld and Johann 2016 , p.129). In a ranking of aspects of reputation of medical scientists, the h-index takes rank 7 (with a mean value of 3.4 with 5 being the best one) out of 17 evaluation criteria. Top-ranked indicators are the reputation of the journals of the scientists’ publications (4.1), the scientists’ citations (4.0), and their publication amount (3.7) (Krempkow et al. 2011 , p. 37). For hiring of psychology professors in Germany, the h-index had factual relevance for the tenure decision with a mean value of 3.64 (on a six-point scale) and ranks on position 12 out of more than 40 criteria for professorship (Abele-Brehm and Bühner 2016 ). Here, the number of peer-reviewed publications is top-ranked (mean value of 5.11). Obviously, these few studies highlight that the h-index indeed has relevance for research evaluation in Germany next to publication and citation numbers.

What is still a research desideratum is an in-depth description of researchers’ personal estimations on the h-index and an analysis of possible differences concerning researchers’ generation, their gender, and the discipline.

What is about the researchers’ state of knowledge on the h-index? Of course, we may ask, “What’s your knowledge on the h-index? Estimate on a scale from 1 to 5!” But personal estimations are subjective and do not substitute a test of knowledge (Kruger and Dunning 1999 ). Knowledge tests on researchers’ state of knowledge concerning the h-index are—to our best knowledge—a research desideratum, too.

In this article, we pursue two goals, namely on the one hand—similar to Buela-Casal and Zych ( 2012 ) on the impact factor—the collection of data about researchers’ personal estimations of the importance of the h-index for themselves as well as their discipline, and on the other hand data on the researchers’ concrete knowledge on the h-index and the way of its calculation. In short, these are our research questions:

RQ1: How do researchers estimate the importance of the h-index?

RQ2: What is the researchers’ knowledge on the h-index?

In order to answer RQ1, we asked researchers on their personal opinions; to answer RQ2, we additionally performed a test of their knowledge.

Online survey

Online-survey-based questionnaires provide a means of generating quantitative data. Furthermore, they ensure anonymity, and thus, a high degree of unbiasedness to bare personal information, preferences, and own knowledge. Therefore, we decided to work with an online survey. As we live and work in Germany, we know well the German academic landscape and thus restricted ourselves to professors working at a German university. We have focused on university professors as sample population (and skipped other academic staff in universities and also professors at universities of applied sciences), because we wanted to concentrate on persons who have (1) an established career path (in contrast to other academic staff) and (2) are to a high extent oriented towards publishing their research results (in contrast to professors at universities of applied science, formerly called Fachhochschulen , i.e. polytechnics, who are primarily oriented towards practice).

The online questionnaire (see Appendix 1 ) in German language contained three different sections. In Sect.  1 , we asked for personal data (gender, age, academic discipline, and university). Section  2 is on the professors’ personal estimations of the importance of publications, citations, their visibility on WoS, Scopus, and Google Scholar, the h-index on the three platforms, the importance of the h-index in their academic discipline, and, finally, their preferences concerning h-index or m-index. We chose those three information services as they are the most prominent general scientific bibliographic information services (Linde and Stock 2011 , p. 237) and all three present their specific h-index in a clearly visible way. Section  3 includes the knowledge test on the h-index and a question concerning the m-index.

In this article, we report on all aspects in relation with the h-index (for other aspects, see Kamrani et al. 2020 ). For the estimations, we used a 5-point Likert scale (from 1: very important via 3: neutral to 5: very unimportant) (Likert 1932 ). It was possible for all estimations to click also on “prefer not to say.” The test in Sect.  3 was composed of two questions, namely a subjective estimation of the own knowledge on the h-index and an objective knowledge test on this knowledge with a multiple-choice test (items: one correct answer, four incorrect ones as distractors, and the option “I’m not sure”). Those were the five items (the third one being counted as correct):

h is the quotient of the number of citations of journal articles in a reference period and the number of published journal articles in the same period;

h is the quotient of the general number of citations of articles (in a period of three years) and the number of citations of a researcher’s articles (in the same three years);

h is the number of articles by a researcher, which were cited h times at minimum;

h is the number of all citations concerning the h-index, thereof subtracted h 2 ;

h is the quotient of the number of citations of a research publication and the age of this publication.

A selected-response format for the objective knowledge test was chosen since it is recommended as the best choice for measuring knowledge (Haladyna and Rodriguez 2013 ). For the development of the knowledge test items we predominantly followed the 22 recommendations given by Haladyna and Rodriguez ( 2013 , in section II). Using a three-option multiple-choice should be superior to the four- or five-option for several reasons. However, we decided to use five options because our test only contained one question. The “I’m not sure” selection was added for the reason that our test is not a typical (classroom) assessment test. We, therefore, did not want to force an answer, for example through guessing, but rather wanted to know if participants do not know the correct answer. Creating reliable distractors can be seen as the most difficult part of the test development. Furthermore, validation is a crucial task. Here we tested and validated the question to the best of our knowledge.

As no ethical review board was involved in our research, we had to determine the ethical harmlessness of the research project ourselves and followed suggestions for ethical research applying online surveys such as consent, risk, privacy, anonymity, confidentiality, and autonomy (Buchanan and Hvizdak 2009 ). We found the e-mail addresses of the participants in a publicly accessible source (a handbook on all German faculty members, Deutscher Hochschulverband 2020 ); the participation was basically voluntary, and the participants knew that their answers became stored. At no time, participants became individually identifiable through our data collection or preparation as we strictly anonymized all questionnaires.

Participants

The addresses of the university professors were randomly extracted from the German Hochschullehrer-Verzeichnis (Deutscher Hochschulverband 2020 ). So, our procedure was non-probability sampling, more precisely convenience sampling in combination with volunteer sampling (Vehovar et al. 2016 ). Starting with volume 1 of the 2020 edition of the handbook, we randomly picked up entries and wrote the e-mails addresses down. The link to the questionnaire was distributed to every single professor by the found e-mail addresses; to host the survey we applied UmfrageOnline . To strengthen the power of the statistical analysis we predefined a minimum of 1000 usable questionnaires. The power tables provided by Cohen ( 1988 ) have a maximum of n  = 1000 participants. Therefore, we chose this value of the sample size to ensure statistically significant results, also for smaller subsets as single genders, generations, and disciplines (Cohen 1992 ). We started the mailing in June 2019 and stopped it in March 2020, when we had response of more than 1000 valid questionnaires. All in all we contacted 5722 professors by mail and arrived at 1081 completed questionnaires, which corresponds to a response rate of 18.9%.

Table 1 shows a comparison between our sample of German professors at universities with the population as one can find it in the official statistics (Destatis 2019 ). There are only minor differences concerning the gender distribution and also few divergences concerning most disciplines; however, Table 1 exhibits two huge differences. In our sample, we find more (natural) scientists than in the official statistics and less scholars in the humanities and the social sciences.

In our analysis, we distinguished always between the results for all participants, and, additionally, the results by gender (Geraci et al. 2015 ), generation (Fietkiewicz et al. 2016 ), and the field of knowledge (Hirsch and Buela-Casal 2014 ). We differentiated two genders (men, women) (note the questionnaire also provided the options “diverse” and “prefer not to say,” which were excluded from further calculations concerning gender), four generations: Generation Y (born after 1980), Generation X (born between 1960 and 1980), Baby Boomers (born after 1946 and before 1960), Silent Generation (born before 1946), and six academic disciplines: (1) geosciences, environmental sciences, agriculture, forestry, (2) humanities, social sciences, (3) sciences (including mathematics), (4) medicine, (5) law, and (6) economics. This division of knowledge fields is in line with the faculty structure of many German universities. As some participants answered some questions with “prefer not to say” (which was excluded from further calculations), the sum of all answers is not always 1081.

As our Likert scale is an ordinal scale, we calculated in each case the median as well as the interquartile range (IQR). For the analysis of significant differences we applied the Mann–Whitney u-test (Mann and Whitney 1947 ) (for the two values of gender) and the Kruskall–Wallis h-test (Kruskal and Wallis 1952 ) (for more than two values as the generations and academic disciplines). The data on the researchers’ knowledge on the h-index are on a nominal scale, so we calculated relative frequencies for three values (1: researcher knows the h-index in her/his self-estimation and passed the test; 2: researcher does not know the h-index in her/his self-estimation; 3: researcher knows the h-index in her/his self-estimation and failed the test) and used chi-squared test (Pearson 1900 ) for the analysis of differences between gender, knowledge area, and generation. We distinguish between three levels of statistical significance, namely *: p  ≤ 0.05 (significant), **: p  ≤ 0.01 (very significant), and ***: p  ≤ 0.001 (extremely significant); however, one has to interpret such values always with caution (Amrhein et al. 2019 ). All calculations were done with the help of SPSS (see a sketch of the data analysis plan in Appendix 2 ).

Researchers’ estimations of the h-index

How do researchers estimate the importance of the h-index for their academic discipline? And how important is the h-index (on WoS, Scopus, and Google Scholar) for themselves? In this paragraph, we will answer our research question 1.

Table 2 shows the different researcher estimations of the importance of the h-index concerning their discipline. While for all participants the h-index is “important” (2) for their academic field (median 2, IQA 1), there are massive and extremely significant differences between the single disciplines. For the sciences, medicine, and geosciences (including environmental sciences, agriculture, and forestry) the h-index is much more important (median 2, IQA 1) than for economics (median 3, IQA 1), humanities and social sciences (median 4, IQA 2), and law (median 5, IQA 0). The most votes for “very important” come from medicine (29.1%), the least from the humanities and social sciences (1.0%) as well as from law (0.0%). Conversely, the most very negative estimations (5: “very unimportant”) can be found among lawyers (78.6%) and scholars from the humanities and social sciences (30.4%). There is a clear cut between sciences (including geosciences, etc., and medicine) on one hand and humanities and all social sciences (including law and economics) on the other hand—with a stark importance of the h-index for the first-mentioned disciplines and a weak importance of the h-index for the latter.

In Tables 3 , 4 and 5 we find the results for the researchers’ estimations of the importance of their h-index on WoS (Table 3 ), Scopus (Table 4 ), and Google Scholar (Table 5 ). For all participants, the h-index on WoS is the most important one (median 2; however, with a wide dispersion of IQR 3), leaving Scopus and Google Scholar behind it (median 3, IQR 2 for both services). For all three bibliographic information services, the estimations of men and women do not differ in the statistical picture. For scientists (including geoscientists, etc.), a high h-index on WoS and Scopus is important (median 2); interestingly, economists join scientists when it comes to the importance of the h-index on Google Scholar (all three disciplines having a median of 2). For scholars from humanities and social sciences, the h-indices on all three services are unimportant (median 4), for lawyers they are even very unimportant (median 5). For researchers in the area of medicine there is a decisive ranking: most important is their h-index on WoS (median 2, IQR 2, and 41.5% votes for “very important”), followed by Scopus (median 2, IQA 1, but only 18.4% votes for “very important”), and, finally, Google Scholar (median 3, IQR 1, and the modus also equals 3, “neutral”). For economists, the highest share of (1)-votes (“very important”) is found for Google Scholar (29.9%) in contrast to the fee-based services WoS (19.7%) and Scopus (12.2%).

Similar to the results of the knowledge areas, there is also a clear result concerning the generations. The older a researcher, the less important is his or her h-index for him- or herself. We see a declining number of (1)-votes in all three information services, and a median moving over the generations from 2 to 3 (WoS), 2 to 4 (Scopus), and 2 to 3 (Google Scholar). The youngest generation has a preference for the h-index on Google Scholar ((1)-votes: 34.9%) over the h-indices on WoS ((1)-votes: 25.9%) and Scopus ((1)-votes: 19.8%).

A very interesting result of our study are the impressive differences of the importance estimations of the h-index by discipline (Fig.  1 ). With three tiny exceptions, the estimations for the general importance and the importance of the h-indices on WoS, Scopus, and Google Scholar are consistent inside each scientific disciplines. For the natural sciences, geosciences etc., and medicine, the h-index is important (median 2), for economics, it is neutral (median 3), for the humanities and social sciences it is unimportant (median 4), and, finally, for law this index is even very unimportant (median 5).

Researchers’ estimations of the h-index by discipline (medians). N  = 1001 (general importance), N  = 961 (WoS), N  = 946 (Scopus), N  = 966 (Google Scholar); Scale: (1) very important, (2) important, (3) neutral, (4) unimportant, (5) very unimportant

We do not want to withhold a by-result on the estimation on a modification of the h-index by the time-adjusted m-index. 567 participants made a decision: for 50.8% of them the h-index is the better one, 49.2% prefer the m-index. More women (61.1%) than men (47.3%) choose the m-index over the original h-index. All academic disciplines except one prefer the m-index; scientists are the exception (only 42.8% approval for the m-index). For members of Generation Y, Baby Boomers, and Silent Generation the m-index is the preferable index; Generation X prefers mainly (54.3%) the h-index. Inside the youngest generation, Generation Y (being discriminated by the h-index), the majority of researchers (65.5%) likes the m-index more than the h-index.

Researchers’ state of knowledge on the h-index

Answering our research question 2, the overall result is presented in Fig.  2 . This is a combination of three questions, as we initially asked the researchers regarding their personal estimations of their general familiarity (Appendix 1 , Q10) and calculation knowledge (Q13) on the h-index. Only participants who confirmed that they have knowledge on the indicators’ calculation (Q10 and Q13) made the knowledge test (Q14). About three fifths of the professors know the h-index in their self-estimations and passed the test, one third of all answering participants does not know the h-index following their self-estimations, and, finally, 7.2% wrongly estimated their knowledge on the h-index, as they failed the test but meant to know it.

Researchers’ state of knowledge on the h-index: The basic distribution. N  = 1017

In contrast to many of our results concerning the researchers’ estimation of the importance of the h-index we see differences in the knowledge on the h-index by gender (Table 6 ). Only 41.6% of the women have justified knowledge (men: 64.6%), 50.0% do not know the definition or the formula of the h-index (men: 28.7%), and 8.3% wrongly estimate their knowledge as sufficient (men: 6.9%). However, these differences are statistically not significant.

In the sciences (incl. geosciences, etc.) and in medicine, more than 70% of the participants do know how to calculate the h-index. Scientists have the highest level of knowledge on the h-index (79.1% passed the knowledge test). Participants from the humanities and social sciences (21.1%) as well as from law (7.1%) exhibit the lowest states of knowledge concerning the h-index. With a share of 48.3%, economists take a middle position between the two main groups of researchers; however, there are 13.8% of economists who wrongly overestimate their knowledge state.

We found a clear result concerning the generations: the older the researcher the less is the knowledge on the h-index. While 62.9% of the Generation X know the calculation of the h-index, only 53.2% of the Baby Boomers possess this knowledge. The differences in the states of the researchers’ knowledge on the h-index within the knowledge areas and generations are extremely significant each.

Main results

Our main results are on the researchers’ estimations of the h-index and their state of knowledge on this scientometric indicator. We found a clear binary division between the academic knowledge fields: For the sciences (including geosciences, agriculture, etc.) and medicine the h-index is important for the researchers themselves and for their disciplines, while for the humanities and social sciences, economics, and law the h-index is considerably less important. For the respondents from the sciences and medicine, the h-index on WoS is most important, followed by the h-index of Google Scholar and Scopus. Surprisingly, for economists Google Scholar’s h-index is very attractive. We did not find significant differences between the estimations of the importance of the h-index between men and women; however, there are differences concerning the generations: the older the participants the less important they estimate the importance of the h-index.

Probably, for older professors the h-index has not the same significance as for their younger colleagues, as they are not so much in need to plan their further career or to apply for new research projects. On average, for researchers aged 60 and more, their productivity declines in contrast to younger colleagues (Kyvik 1990 ). And perhaps some of them simply do not know the existence of more recent services and of new scientometric indicators. Younger researchers are more tolerant of novelty in their work (Packalen and Bhattachrya 2015 ), and such novelty includes new information services (as Scopus and Google Scholar) as well as new indicators (as the h-index). It is known that young researchers rely heavily on search engines like Google (Rowlands et al. 2008 ), which partly may explain the high values for Google Scholar especially from Generation Y. Furthermore, the increasing publication pressure and the h-index utilization for decisions about early career researchers’ work-related paths thus also impact the importance of the indicator for those young professors (Farlin and Majewski 2013 ).

All in all, two fifths of the professors do not know the concrete calculation of the h-index or—which is rather scary—wrongly deem to know what the h-index is and failed our simple knowledge test. The women do even worse, as only about two fifths really know what the h-index is and how it is defined and calculated, but we should have in mind that this gender difference is statistically not significant. The older the researcher, the higher is the share of participants who do not know the definition and calculation of the h-index. The researchers’ knowledge on the h-index is much smaller in the academic disciplines of the humanities and the social sciences.

The h-index in the academic areas

Especially the obvious differences between the academic areas demand further explanation. Participants from the natural sciences and from medicine estimate the importance of the h-index as “important” or even “very important,” and they know details on this indicator to a high extend. The participants from the humanities, the social sciences, economics, and law are quite different. They estimate the h-index’ importance as “neutral,” “unimportant,” or even as “very unimportant,” and the share of researchers with profound knowledge on the h-index is quite low. Haddow and Hammarfelt ( 2019 ) also report a lower use of the h-index within these fields. Similar to our study, especially researchers in the field of law ( n  = 24) did not make use of the h-index. All researchers publish and all cite, too. There are differences in their publication channels, as scientists publish mostly in journals and researchers from the humanities publish in monographs and sometimes also in journals (Kulczycki et al. 2018 ), but this may not explain the differences concerning the importance of and the knowledge state on the h-index. Furthermore, more information on how such researchers’ h-index perceptions through different disciplines comply with the h-index (mis)usage for research evaluation within those disciplines would add another dimension to this topic.

The indeed very large general information services WoS and Scopus are, compared to personal literature lists of researchers, quite incomplete (Hilbert et al. 2015 ). There is also a pronounced unequal coverage of certain disciplines (Mongeon and Paul-Hus 2016 ) and many languages (except English) (Vera-Baceta et al. 2019 ). Perhaps these facts, in particular, prevent representatives of the disadvantaged disciplines and languages (including German—and we asked German professors) from a high estimation of the relevance of their h-index as important on these platforms. Then, however, the rejection of the h-index of Google Scholar, which can also be seen, is surprising, because this information service is by far the most complete (Martin-Martin et al. 2018 ). However, economists are very well informed here, as they—as the only academic representatives—highly value their h-index at Google Scholar. On the other hand, the use of Google Scholar for research evaluation is discussed in general. Although its coverage is usually broader than those provided by more controlled databases and steadily expanding its collection, there exist widely known issues, for example, its low accuracy (Halevi et al. 2017 ). Depending on a researcher’s own opinion on this topic, this could be a reason for seeing no importance in the h-index provided by Google Scholar as well.

Another attempt for an explanation may be the different cultures in the different research areas. For Kagan ( 2009 , p. 4), natural scientists see their main interest in explanation and prediction, while for humanists it is understanding (following Snow 1959 and Dilthey 1895 , p. 10). The h-index is called an indicator allowing explanation and prediction of scientific achievement (Hirsch 2007 ); it is typical for the culture of natural sciences. Researchers from the natural science and from medicine are accustomed to numbers, while humanists seldom work quantitatively. In the humanities, other indicators such as book reviews and the quality of book publishers are components for their research evaluation; however, such aspects are not reflected by the h-index. And if humanities scholars are never asked for their h-index, why should they know or use it?

Following Kagan ( 2009 , p. 5) a second time, humanists exhibit only minimal dependence on outside support and natural scientists are highly dependent on external sources of financing. The h-index can work as an argument for the allocation of outside support. So for natural scientists the h-index is a very common fabric and they need it for their academic survival; humanists are not as familiar with numerical indicators and for them the h-index is not so much-needed as for their colleagues from the science and medicine faculties. However, this dichotomous classification of research and researchers may be an oversimplifying solution (Kowalski and Mrdjenovich 2016 ) and there is a trend in consulting and using such research evaluation indicators in the humanities and social sciences, too. For preparing a satisfying theory of researchers’ behavior concerning the h-index (or, in general, concerning scientometric indicators)—also in dependence on their background in an academic field—more research is needed.

Limitations, outlook, and recommendations

A clear limitation of the study is our studied population, namely university professors from Germany. Of course, researchers in other countries should be included in further studies. It seems necessary to broaden the view towards all researchers and all occupational areas, too, including, for instance, also lecturers in polytechnics and researchers in private companies. Another limitation is the consideration of only three h-indices (of WoS, Scopus, and Google Scholar). As there are other databases for the calculation of an h-index (e.g., ResearchGate) the study should be broadened to all variants of the h-index.

Another interesting research question may be: Are there any correlations between the estimations of the importance of the h-index or the researcher’s knowledge on the h-index and the researcher’s own h-index? Does a researcher with a high h-index on, for instance, WoS, estimate the importance of this indicator higher than a researcher with a low h-index? Hirsch ( 2020 ) speculates that people with high h-indexes are more likely to think that this indicator is important. A more in-depth analysis on the self-estimation of researchers’ h-index knowledge might also consider the Dunning-Kruger effect, showing certain people can be wrongly confident about their limited knowledge within a domain and not having the ability to realize this (Kruger and Dunning 1999 ).

As the h-index has still an important impact on the evaluation of scientists and as not all researchers are very knowledgeable about this author-specific research indicator, it seems to be a good idea to strengthen their knowledge in the broader area of “metric-wiseness” (Rousseau et al. 2018 ; Rousseau and Rousseau 2015 ). With a stronger focus on educating researchers and research support staff in terms of the application and interpretation of metrics as well as to reduce misuse of indicators, Haustein ( 2018 ) speaks about better (scholarly) “metrics literacies.” Following Hammarfelt and Haddow ( 2018 ), we should further discuss possible effects of indicators within the “metrics culture.” Likewise, this also applies to all knowledgeable researchers as well as research evaluators who also may or may not be researchers by themselves. Here, the focus rather lies to raise awareness for metrics literacies and to foster fair research evaluation practices not incorporating any kind of misuse. This leads directly to a research gap in scientometrics. Further research on concrete data about the level of researchers’ knowledge not only concerning the h-index, but also on other indicators such as WoS’s impact factor, Google’s i-10 index, Scopus’ CiteScore, the source normalized impact per paper (SNIP), etc., also in a comparative perspective would draw a more comprehensive picture on the current indicator knowledge. All the meanwhile “classical” scientometric indicators are based upon publication and citation measures (Stock 2001 ). Alternative indicators are available today, which are based upon social media metrics, called “altmetrics” (Meschede and Siebenlist 2018 ; Thelwall et al. 2013 ). How do researchers estimate the importance of these alternative indicators and do they know their definitions and their formulae of calculation? First insights on this give Lemke et al. ( 2019 ), also in regard to researchers’ personal preferences and concerns.

Following Hirsch ( 2020 ), the h-index is by no means a valid indicator of research quality; however, it is very common especially in the sciences and medicine. Probably, it is a convenient indicator for some researchers who want to avoid the hassle of laborious and time-consuming reviewing and scrutinizing other researchers’ œuvre. Apart from its convenience and popularity, and seen from an ethical perspective, one should consider what significance a single metric should have and how we—in general—want to further shape the future of research evaluation.

Abele-Brehm, A., & Bühner, M. (2016). Wer soll die Professur bekommen? Eine Untersuchung zur Bewertung von Auswahlkriterien in Berufungsverfahren der Psychologie. Psychologische Rundschau, 67 , 250–261. https://doi.org/10.1026/0033-3042/a000335 .

Article   Google Scholar  

Aksnes, D. W., & Rip, A. (2009). Researchers’ perceptions of citations. Research Policy, 38 (6), 895–905. https://doi.org/10.1016/j.respol.2009.02.001 .

Amrhein, V., Greenland, S., & McShane, B. (2019). Retire statistical significance. Nature, 567 (7748), 305–307. https://doi.org/10.1038/d41586-019-00857-9 .

Askeridis, J. (2018). An h index for Mendeley: Comparison of citation-based h indices and a readership-based h men index for 29 authors. Scientometrics, 117 , 615–624. https://doi.org/10.1007/s11192-018-2882-8 .

Bar-Ilan, J. (2008). Which h-index?—A comparison of WoS, Scopus and Google Scholar. Scientometrics, 74 (2), 257–271. https://doi.org/10.1007/s11192-008-0216-y .

Bornmann, L., Mutz, R., & Daniel, H.-D. (2008). Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine. Journal of the American Society for Information Science and Technology, 59 (5), 830–837. https://doi.org/10.1002/asi.20806 .

Bornmann, L., Mutz, R., Hug, S. E., & Daniel, H.-D. (2011). A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants. Journal of Informetrics, 5 , 346–359. https://doi.org/10.1016/j.joi.2011.01.006 .

Buchanan, E. A., & Hvizdak, E. E. (2009). Online survey tools: Ethical and methodological concerns of human research ethics committees. Journal of Empirical Research on Human Research Ethics, 4 (2), 37–48. https://doi.org/10.1525/jer.2009.4.2.37 .

Buela-Casal, G., & Zych, I. (2012). What do the scientists think about the impact factor? Scientometrics, 92 , 281–292. https://doi.org/10.1007/s11192-012-0676-y .

Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., & Herrera, F. (2009). H-index: A review focused in its variants, computation and standardization for different scientific fields. Journal of Informetrics, 3 (4), 273–289. https://doi.org/10.1016/j.joi.2009.04.001 .

Chen, C. M.-L., & Lin, W.-Y. C. (2018). What indicators matter? The analysis of perception toward research assessment indicators and Leiden Manifesto. The case study of Taiwan. In R. Costas, T. Franssen, & A. Yegros-Yegros (Eds.), Proceedings of the 23rd International Conference on Science and Technology Indicators (STI 2018) (12–14 September 2018) (pp. 688–698). Leiden, NL: Centre for Science and Technology Studies (CWTS). https://openaccess.leidenuniv.nl/bitstream/handle/1887/65192/STI2018_paper_121.pdf?sequence=1.

Cohen, J. (1988). Statistical power analysis for the behavioral science . (2nd ed.). Hillsdale: Lawrence Erlbaum. https://doi.org/10.4324/9780203771587 .

Book   MATH   Google Scholar  

Cohen, J. (1992). A power primer. Psychological Bulletin, 112 (1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155 .

Costas, R., & Bordons, M. (2007). The h-index: Advantages, limitations and its relation with other bibliometric indicators at the micro level. Journal of Informetrics, 1 (3), 193–203. https://doi.org/10.1016/j.joi.2007.02.001 .

da Silva, J. A. T., & Dobranszki, J. (2018). Multiple versions of the h-index: Cautionary use for formal academic purposes. Scientometrics, 115 (2), 1107–1113. https://doi.org/10.1007/s11192-018-2680-3 .

Derrick, G. E., & Gillespie, J. (2013). A number you just can’t get away from: Characteristics of adoption and the social construction of metric use by researchers. In S. Hinze & A. Lottman (Eds.), Proceedings of the 18th International Conference on Science and Technology Indicators (pp. 104–116). Berlin, DE: Institute for Research Information and Quality Assurance. http://www.forschungsinfo.de/STI2013/download/STI_2013_Proceedings.pdf.

Destatis. (2019). Bildung und Kultur. Personal an Hochschulen (Fachserie 11, Reihe 4.4). Wiesbaden, Germany: Statistisches Bundesamt. https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Hochschulen/Publikationen/Downloads-Hochschulen/personal-hochschulen-2110440187004.html.

Deutscher Hochschulverband. (2020). Hochschullehrer-Verzeichnis 2020, Band 1: Universitäten Deutschland. 28th Ed. Berlin, New York: De Gruyter Saur. https://db.degruyter.com/view/product/549953.

Dilthey, W. (1895). Ideen über eine beschreibende und zergliedernde Psychologie. Sitzungsberichte der königlich preussischen Akademie der Wissenschaften zu Berlin, 7. Juni 1894, Ausgabe XXVI, Sitzung der philosophisch historischen Classe , 1–88. http://www.uwe-mortensen.de/Dilthey%20Ideen%20beschreibendezergliederndePsychologie.pdf.

Ding, J., Liu, C., & Kandonga, G. A. (2020). Exploring the limitations of the h-index and h-type indexes in measuring the research performance of authors. Scientometrics, 122 (3), 1303–1322. https://doi.org/10.1007/s11192-020-03364-1 .

Dinis-Oliveira, R. J. (2019). The h-index in life and health sciences: Advantages, drawbacks and challenging opportunities. Current Drug Research Reviews, 11 (2), 82–84. https://doi.org/10.2174/258997751102191111141801 .

Dorsch, I. (2017). Relative visibility of authors’ publications in different information services. Scientometrics, 112 , 917–925. https://doi.org/10.1007/s11192-017-2416-9 .

Dorsch, I., Askeridis, J., & Stock, W. G. (2018). Truebounded, overbounded, or underbounded? Scientists’ personal publication lists versus lists generated through bibliographic information services. Publications, 6 (1), 1–9. https://doi.org/10.3390/publications6010007 .

Farlin, J., & Majewski, M. (2013). Performance indicators: The educational effect of publication pressure on young researchers in environmental sciences. Environmental Science and Technology, 47 (6), 2437–2438. https://doi.org/10.1021/es400677m .

Fietkiewicz, K. J., Lins, E., Baran, K. S., & Stock, W. G. (2016). Inter-generational comparison of social media use: Investigating the online behavior of different generational cohorts. In Proceedings of the 49th Hawaii international conference on system sciences (pp. 3829–3838). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/HICSS.2016.477.

Geraci, L., Balsis, S., & Busch, A. J. B. (2015). Gender and the h index in psychology. Scientometrics, 105 (3), 2023–2043. https://doi.org/10.1007/s11192-015-1757-5 .

Haddow, G., & Hammarfelt, B. (2019). Quality, impact, and quantification: Indicators and metrics use by social scientists. Journal of the Association for Information Science and Technology, 70 (1), 16–26. https://doi.org/10.1002/asi.24097 .

Haladyna, T. M., & Rodriguez, M. C. (2013). Developing and validating test items . New York: Routledge. https://doi.org/10.4324/9780203850381 .

Book   Google Scholar  

Halevi, G., Moed, H., & Bar-Ilan, J. (2017). Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation. Review of the literature. Journal of Informetrics, 11 (3), 823–834. https://doi.org/10.1016/j.joi.2017.06.005 .

Hammarfelt, B., & Haddow, G. (2018). Conflicting measures and values: How humanities scholars in Australia and Sweden use and react to bibliometric indicators. Journal of the Association for Information Science and Technology, 69 (7), 924–935. https://doi.org/10.1002/asi.24043 .

Haustein, S. (2018). Metrics literacy [Blog post]. https://stefaniehaustein.com/metrics-literacy/

Haustein, S., & Larivière, V. (2015). The use of bibliometrics for assessing research: Possibilities, limitations and adverse effects. In I. Welpe, J. Wollersheim, S. Ringelhan, & M. Osterloh (Eds.), Incentives and performance: Governance of research organizations (pp. 121–139). Cham, CH: Springer. https://doi.org/10.1007/978-3-319-09785-5_8

Hilbert, F., Barth, J., Gremm, J., Gros, D., Haiter, J., Henkel, M., Reinhardt, W., & Stock, W. G. (2015). Coverage of academic citation databases compared with coverage of social media: Personal publication lists as calibration parameters. Online Information Review, 39 (2), 255–264. https://doi.org/10.1108/OIR-07-2014-0159 .

Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102 (46), 16569–16572. https://doi.org/10.1073/pnas.0507655102 .

Article   MATH   Google Scholar  

Hirsch, J. E. (2007). Does the h index have predictive power? Proceedings of the National Academy of Sciences of the United States of America, 104 (49), 19193–19198. https://doi.org/10.1073/pnas.0707962104 .

Hirsch, J. E. (2020). Superconductivity, What the h? The emperor has no clothes. Physics and Society, 49 (1), 4–9.

Google Scholar  

Hirsch, J. E., & Buela-Casal, G. (2014). The meaning of the h-index. International Journal of Clinical and Health Psychology, 14 (2), 161–164. https://doi.org/10.1016/S1697-2600(14)70050-X .

Hu, G. Y., Wang, L., Ni, R., & Liu, W. S. (2020). Which h-index? An exploration within the Web of Science. Scientometrics, 123 , 1225–1233. https://doi.org/10.1007/s11192-020-03425-5 .

Jan, R., & Ahmad, R. (2020). H-index and its variants: Which variant fairly assess author’s achievements. Journal of Information Technology Research, 13 (1), 68–76. https://doi.org/10.4018/JITR.2020010105 .

Jappe, A. (2020). Professional standards in bibliometric research evaluation? A meta-evaluation of European assessment practice 2005–2019. PLoSONE, 15 (4), 1–23. https://doi.org/10.1371/journal.pone.0231735 .

Kagan, J. (2009). The three cultures. Natural sciences, social sciences, and the humanities in the 21st century . Cambridge, MA: Cambridge University Press. https://www.cambridge.org/de/academic/subjects/psychology/psychology-general-interest/three-cultures-natural-sciences-social-sciences-and-humanities-21st-century?format=HB&isbn=9780521518420.

Kamrani, P., Dorsch, I., & Stock, W. G. (2020). Publikationen, Zitationen und H-Index im Meinungsbild deutscher Universitätsprofessoren. Beiträge zur Hochschulforschung, 42 (3), 78–98. https://www.bzh.bayern.de/fileadmin/user_upload/Publikationen/Beitraege_zur_Hochschulforschung/2020/3_2020_Kamrani-Dorsch-Stock.pdf .

Kelly, C. D., & Jennions, M. D. (2006). The h index and career assessment by numbers. Trends in Ecology and Evolution, 21 (4), 167–170. https://doi.org/10.1016/j.tree.2006.01.005 .

Kowalski, C. J., & Mrdjenovich, A. J. (2016). Beware dichotomies. Perspectives in Biology and Medicine, 59 (4), 517–535. https://doi.org/10.1353/pbm.2016.0045 .

Krempkow, R., Schulz, P., Landrock, U., & Neufeld, J. (2011). Die Sicht der Professor/innen auf die Leistungsorientierte Mittelvergabe an Medizinischen Fakultäten in Deutschland . Berlin: iFQ–Institut für Forschungsinformation und Qualitätssicherung. http://www.forschungsinfo.de/Publikationen/Download/LOM_Professorenbefragung.pdf.

Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77 (6), 1121–1134. https://doi.org/10.1037/0022-3514.77.6.1121 .

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47 (260), 583–621. https://doi.org/10.1080/01621459.1952.10483441 .

Kulczycki, E., Engels, T. C. E., Pölönen, J., Bruun, K., Dušková, M., Guns, R., Nowotniak, R., Petr, M., Sivertsen, G., Istenič Starčič, A., & Zuccala, A. (2018). Publication patterns in the social sciences and humanities: Evidence from eight European countries. Scientometrics, 116 (1), 463–486. https://doi.org/10.1007/s11192-018-2711-0 .

Kyvik, S. (1990). Age and scientific productivity. Differences between fields of learning. Higher Education, 19 , 37–55. https://doi.org/10.1007/BF00142022 .

Lemke, S., Mehrazar, M., Mazarakis, A., & Peters, I. (2019). “When you use social media you are not working”: Barriers for the use of metrics in Social Sciences. Frontiers in Research Metrics and Analytics, 3 (39), 1–18. https://doi.org/10.3389/frma.2018.00039 .

Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22 (140), 5–55.

Linde, F., & Stock, W. G. (2011). Information markets . Berlin, New York: De Gruyter Saur. https://doi.org/10.1515/9783110236101 .

Ma, L., & Ladisch, M. (2019). Evaluation complacency or evaluation inertia? A study of evaluative metrics and research practices in Irish universities. Research Evaluation, 28 (3), 209–217. https://doi.org/10.1093/reseval/rvz008 .

Mann, H., & Whitney, D. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18 (1), 50–60. https://doi.org/10.1214/aoms/1177730491 .

Article   MathSciNet   MATH   Google Scholar  

Martin-Martin, A., Orduna-Malea, E., Thelwall, M., & Lopez-Cozar, E. D. (2018). Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12 (4), 1160–1177. https://doi.org/10.1016/j.joi.2018.09.002 .

Meschede, C., & Siebenlist, T. (2018). Cross-metric compatibility and inconsistencies of altmetrics. Scientometrics, 115 (1), 283–297. https://doi.org/10.1007/s11192-018-2674-1 .

Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106 (1), 213–228. https://doi.org/10.1007/s11192-015-1765-5 .

Neufeld, J., & Johann, D. (2016). Wissenschaftlerbefragung 2016. Variablenbericht – Häufigkeitsauszählung . Berlin: Deutsches Zentrum für Hochschul- und Wissenschaftsforschung. https://www.volkswagenstiftung.de/sites/default/files/downloads/Wissenschaftlerbefragung%202016%20-%20Variablenbericht%20-%20H%C3%A4ufigkeitsausz%C3%A4hlungen.pdf

Packalen, M., & Bhattacharya, J. (2015). Age and the trying out of new ideas. Cambridge, MA: National Bureau of Economic Research. (NBER Working Paper Series; 20920). http://www.nber.org/papers/w20920.

Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. Series, 50 (302), 157–175.

Penny, D. (2016). What matters where? Cultural and geographical factors in science. Slides presented at 3rd altmetrics conference, Bucharest, 2016. https://figshare.com/articles/What_matters_where_Cultural_and_geographical_factors_in_science/3969012 .

Rousseau, R., Egghe, L., & Guns, R. (2018). Becoming metric-wise: A bibliometric guide for researchers . Cambridge, MA: Chandos.

Rousseau, S., & Rousseau, R. (2015). Metric-wiseness. Journal of the Association for Information Science and Technology, 66 (11), 2389. https://doi.org/10.1002/asi.23558 .

Rousseau, S., & Rousseau, R. (2017). Being metric-wise: Heterogeneity in bibliometric knowledge. El Profesional de la Informatión, 26 (3), 480–487.

Rowlands, I., Nicholas, D., William, P., Huntington, P., Fieldhouse, M., Gunter, B., Withey, R., Jamali, H. R., Dobrowolski, T., & Tenopir, C. (2008). The Google generation: The information behaviour of the researcher of the future. Aslib Proceedings, 60 (4), 290–310. https://doi.org/10.1108/00012530810887953 .

Snow, C. P. (1959). The two cultures and the scientific revolution . Cambridge: Cambridge University Press.

Stock, W. G. (2001). Publikation und Zitat. Die problematische Basis empirischer Wissenschaftsforschung. Köln: Fachhochschule Köln; Fachbereich Bibliotheks- und Informationswesen (Kölner Arbeitspapiere zur Bibliotheks- und Informationswissenschaft; 29). https://epb.bibl.th-koeln.de/frontdoor/deliver/index/docId/62/file/Stock_Publikation.pdf.

Stock, W. G., & Stock, M. (2013). Handbook of information science . De Gruyter Saur. https://doi.org/10.1515/9783110235005 .

Sugimoto, C. R., & Larivière, V. (2018). Measuring research: What everyone needs to know . New York: Oxford University Press.

Tetzner, R. (2019). What is a good h-index required for an academic position? [Blog post]. https://www.journal-publishing.com/blog/good-h-index-required-academic-position/.

Thelwall, M., Haustein, S., Larivière, V., & Sugimoto, C. R. (2013). Do altmetrics work? Twitter and ten other social web services. PLoS ONE, 8 (5), e64841. https://doi.org/10.1371/journal.pone.0064841 .

Vehovar, V., Toepoel, V., & Steinmetz, S. (2016). Non-probability sampling. In C. Wolf, D. Joye, T. W. Smith, & Y.-C. Fu (Eds.), The SAGE handbook of survey methodology. (pp. 327–343). London: Sage. https://doi.org/10.4135/9781473957893.n22 .

Chapter   Google Scholar  

Vera-Baceta, M. A., Thelwall, M., & Kousha, K. (2019). Web of science and Scopus language coverage. Scientometrics, 121 (3), 1803–1813. https://doi.org/10.1007/s11192-019-03264-z .

Waltman, L., & van Eck, N. J. (2012). The inconsistency of the h-index. Journal of the American Society for Information Science and Technology, 63 (2), 406–415. https://doi.org/10.1002/asi.21678 .

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Kamrani, P., Dorsch, I. & Stock, W.G. Do researchers know what the h-index is? And how do they estimate its importance?. Scientometrics 126 , 5489–5508 (2021). https://doi.org/10.1007/s11192-021-03968-1

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How to Construct an Index for Research

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An index is a composite measure of variables, or a way of measuring a construct--like religiosity or racism--using more than one data item. An index is an accumulation of scores from a variety of individual items. To create one, you must select possible items, examine their empirical relationships, score the index, and validate it.

Item Selection

The first step in creating an index is selecting the items you wish to include in the index to measure the variable of interest. There are several things to consider when selecting the items. First, you should select items that have face validity. That is, the item should measure what it is intended to measure. If you are constructing an index of religiosity, items such as church attendance and frequency of prayer would have face validity because they appear to offer some indication of religiosity.

A second criterion for choosing which items to include in your index is unidimensionality. That is, each item should represent only one dimension of the concept you are measuring. For example, items reflecting depression should not be included in items measuring anxiety, even though the two might be related to one another.

Third, you need to decide how general or specific your variable will be. For example, if you only wish to measure a specific aspect of religiosity, such as ritual participation, then you would only want to include items that measure ritual participation, such as church attendance, confession, communion, etc. If you are measuring religiosity in a more general way, however, you would want to also include a more balanced set of items that touch on other areas of religion (such as beliefs, knowledge, etc.).

Lastly, when choosing which items to include in your index, you should pay attention to the amount of variance that each item provides. For example, if an item is intended to measure religious conservatism, you need to pay attention to what proportion of respondents would be identified as religiously conservative by that measure. If the item identifies nobody as religiously conservative or everyone as a religiously conservative, then the item has no variance and it is not a useful item for your index.

Examining Empirical Relationships

The second step in index construction is to examine the empirical relationships among the items you wish to include in the index. An empirical relationship is when respondents’ answers to one question help us predict how they will answer other questions. If two items are empirically related to each other, we can argue that both items reflect the same concept and we can, therefore, include them in the same index. To determine if your items are empirically related, crosstabulations, correlation coefficients , or both may be used.

Index Scoring

The third step in index construction is scoring the index. After you have finalized the items you are including in your index, you then assign scores for particular responses, thereby making a composite variable out of your several items. For example, let’s say you are measuring religious ritual participation among Catholics and the items included in your index are church attendance, confession, communion, and daily prayer, each with a response choice of "yes, I regularly participate" or "no, I do not regularly participate." You might assign a 0 for "does not participate" and a 1 for "participates." Therefore, a respondent could receive a final composite score of 0, 1, 2, 3, or 4 with 0 being the least engaged in Catholic rituals and 4 being the most engaged.

Index Validation

The final step in constructing an index is validating it. Just like you need to validate each item that goes into the index, you also need to validate the index itself to make sure that it measures what it is intended to measure. There are several methods for doing this. One is called item analysis in which you examine the extent to which the index is related to the individual items that are included in it. Another important indicator of an index’s validity is how well it accurately predicts related measures. For example, if you are measuring political conservatism, those who score the most conservative in your index should also score conservative in other questions included in the survey.

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How to Write an Index

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This article was co-authored by Christopher Taylor, PhD and by wikiHow staff writer, Jennifer Mueller, JD . Christopher Taylor is an Adjunct Assistant Professor of English at Austin Community College in Texas. He received his PhD in English Literature and Medieval Studies from the University of Texas at Austin in 2014. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 2,009,121 times.

An index is an alphabetical list of keywords contained in the text of a book or other lengthy writing project. It includes pointers to where those keywords or concepts are mentioned in the book—typically page numbers, but sometimes footnote numbers, chapters, or sections. The index can be found at the end of the work, and makes a longer nonfiction work more accessible for readers, since they can turn directly to the information they need. Typically you'll start indexing after you've completed the main writing and research. [1] X Research source

Preparing Your Index

• Typically, if you index from a hard copy you'll have to transfer your work to a digital file. If the work is particularly long, try to work straight from the computer so you can skip this extra step.

• If footnotes or endnotes are merely source citations, they don't need to be included in the index.
• Generally, you don't need to index glossaries, bibliographies, acknowledgements, or illustrative items such as charts and graphs.
• If you're not sure whether something should be indexed, ask yourself if it contributes something substantial to the text. If it doesn't, it typically doesn't need to be indexed.

• In most cases, if you have a "works cited" section appearing at the end of your text you won't need to index authors. You would still include their names in the general index, however, if you discussed them in the text rather than simply citing their work.

• For example, if you're writing a book on bicycle maintenance, you might have index cards for "gears," "wheels," and "chain."
• Put yourself in your reader's shoes, and ask yourself why they would pick up your book and what information they would likely be looking for. Chapter or section headings can help guide you as well.

• For example, a dessert cookbook that included several types of ice cream might have one entry for "ice cream," followed by subentries for "strawberry," "chocolate," and "vanilla."
• Treat proper nouns as a single unit. For example, "United States Senate" and "United States House of Representatives" would be separate entries, rather than subentries under the entry "United States."

• Stick to nouns and brief phrases for subentries, avoiding any unnecessary words.
• For example, suppose you are writing a book about comic books that discusses Wonder Woman's influence on the feminist movement. You might include a subentry under "Wonder Woman" that says "influence on feminism."

• For example, if you were writing a dessert cookbook, you might have entries for "ice cream" and "sorbet." Since these frozen treats are similar, they would make good cross references of each other.

Formatting Entries and Subentries

• The style guide provides specifics for you in terms of spacing, alignment, and punctuation of your entries and subentries.

• For example, an entry in the index of a political science book might read: "capitalism: 21st century, 164; American free trade, 112; backlash against, 654; expansion of, 42; Russia, 7; and television, 3; treaties, 87."
• If an entry contains no subentries, simply follow the entry with a comma and list the page numbers.

• People's names typically are listed alphabetically by their last name. Put a comma after the last name and add the person's first name.
• Noun phrases typically are inverted. For example, "adjusting-height saddle" would be listed in an index as "saddle, adjusting-height." [8] X Research source

• Avoid repeating words in the entry in the subentries. If several subentries repeat the same word, add it as a separate entry, with a cross reference back to the original entry. For example, in a dessert cookbook you might have entries for "ice cream, flavors" and "ice cream, toppings."
• Subentries typically are listed alphabetically as well. If subentry terms have symbols, hyphens, slashes, or numbers, you can usually ignore them.

• If a proper name, such as the name of a book or song, includes a word such as "a" or "the" at the beginning of the title, you can either omit it or include it after a comma ("Importance of Being Earnest, The"). Check your style guide for the proper rule that applies to your index, and be consistent.

• When listing a series of pages, if the first page number is 1-99 or a multiple of 100, you also use all of the digits. For example, "ice cream: vanilla, 100-109."
• For other numbers, you generally only have to list the digits that changed for subsequent page numbers. For example, "ice cream: vanilla, 112-18."
• Use the word passim if references are scattered over a range of pages. For example, "ice cream: vanilla, 45-68 passim . Only use this if there are a large number of references within that range of pages.

• Place a period after the last page number in the entry, then type See also in italics, with the word "see" capitalized. Then include the name of the similar entry you want to use.
• For example, an entry in an index for a dessert cookbook might contain the following entry: "ice cream: chocolate, 4, 17, 24; strawberry, 9, 37; vanilla, 18, 25, 32-35. See also sorbet."

• For example, a beginning cyclist may be looking in a manual for "tire patches," which are called "boots" in cycling terms. If you're writing a bicycle manual aimed at beginners, you might include a "see" cross reference: "tire patches, see boots."

Editing Your Index

• You'll also want to search for related terms, especially if you talk about a general concept in the text without necessarily mentioning it by name.

• If you have any entries that are too complex or that might confuse your readers, you might want to simplify them or add a cross reference.
• For example, a bicycle maintenance text might discuss "derailleurs," but a novice would more likely look for terms such as "gearshift" or "shifter" and might not recognize that term.

• For example, you might include an entry in a dessert cookbook index that read "ice cream, varieties of: chocolate, 54; strawberry, 55; vanilla, 32, 37, 56. See also sorbet."

• Generally, an entry should occur on two or three page numbers. If it's only found in one place, you may not need to include it at all. If you decide it is necessary, see if you can include it as a subentry under a different entry.
• For example, suppose you are indexing a dessert cookbook, and it has ice cream on two pages and sorbet on one page. You might consider putting these together under a larger heading, such as "frozen treats."

• You may want to run searches again to make sure the index is comprehensive and includes as many pointers as possible to help guide your readers.

• Make sure any cross references match the exact wording of the entry or entries they reference.

• Indexes are typically set in 2 columns, using a smaller font than that used in the main text. Entries begin on the first space of the line, with the subsequent lines of the same entry indented.

Expert Q&A

• If creating an index seems like too large of a task for you to complete on your own by the publisher's deadline, you may be able to hire a professional indexer to do the work for you. Look for someone who has some knowledge and understanding about the subject matter of your work. Thanks Helpful 0 Not Helpful 0
• Make the index as clear and simple as you can. Readers don't like looking through a messy, hard-to-read index. Thanks Helpful 0 Not Helpful 0

• If you're using a word processing app that has an indexing function, avoid relying on it too much. It will index all of the words in your text, which will be less than helpful to readers. [15] X Research source Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ https://ugapress.org/resources/for-authors/indexing-guidelines/
• ↑ https://www.hup.harvard.edu/resources/authors/pdf/hup-author-guidelines-indexing.pdf
• ↑ https://www.press.uchicago.edu/Misc/Chicago/CHIIndexingComplete.pdf
• ↑ https://edinburghuniversitypress.com/publish-with-us/from-manuscript-to-finished-book/preparing-your-index

About This Article

An index is an alphabetical list of keywords found in a book or other lengthy writing project. It will have the chapters or page numbers where readers can find that keyword and more information about it. Typically, you’ll write your index after you’ve completed the main writing and research. In general, you’ll want to index items that are nouns, like ideas, concepts, and things, that add to the subject of the text. For example, a dessert cookbook might have an entry for “ice cream” followed by subentries for “strawberry,” “chocolate,” and “vanilla.” To learn how to format your index entries, keep reading! Did this summary help you? Yes No

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Should a Ph.D. be done with a low h-indexed professor

I live in a 3rd world country and at one of top universities in my country a professor offered me a PhD position. I will pursue a part time PhD while working full time. I have to decide on whether or not to accept his offer.

My PhD chances in U.S. or other western countries where an established academic community exists is infinitesimal. This is due to my undergraduate degree is from another nationally lower ranked school than the nationally top ranked university I mentioned. Professors at my undergraduate school have no connection with western researchers and no cares about them, my undergradute professors also do not care about the international academic community.

Though the institution I was offered to pursue a PhD is nationally reputed and having a PhD degree from there carries a nationwise reputation, I believe that my postdoc chances from decent to good schools in western countries are very low. My professor has a title as Professor, but his h-index is extremely low (< 10), while his western colleagues usually have an index of greater than 30, and usually renowed ones have an index of gretaer than 50. Also his students do not seem to secure good postdocs.

I have started to dislike my professor too. I may work with him a few years and apply for a PhD after obtaining some publications, but still I will need his connections.

This might be my only chance for a PhD and I am not sure what to do. What are my chances in western academic system after this PhD ? Should a PhD done with a professor whose work does not receive much citations and who publishes rarely ?

• publications

• 1 Welcome to Academia.SE! However, this post addresses a few different questions at once. Please consider scoping question to address a single issue (for example, only about low h index OR only about professor in a third world country with not much collaboration with Western research). You can ask a few different questions, but please, do it in separate threads. That way, it will be possible to answer your questions in a meaningful way and answers will be usable also for others. –  Piotr Migdal Commented Mar 1, 2014 at 12:16
• What do you wish to do with your PHd once you get it? –  Ian Commented Mar 1, 2014 at 17:24
• I would advise you to speak to other PhD students who have done it part-time. It's difficult if you're doing it full-time, and I've personally tried it part-time and wasn't able to do it. Not saying it's not possible, and if it's your only option you need to do what you need to do. Just saying be prepared by talking to others who have done it. –  user12527 Commented Mar 2, 2014 at 0:16
• 2 I don't know if you can do this, but if your main goal is to end up in Western academia, you might consider doing a masters in a Western country. I did that and it made a world of a difference when it came to applying to PhD positions. –  Ana Commented Mar 2, 2014 at 8:34

6 Answers 6

Picking a PhD supervisor based on his h-index is like picking a car based on his horse-power; you ignore a huge number of factors that are probably equally important if not more. It is drivable (can you work with this person?), is it expensive to run (do the guy needs to pampered and treated like royalty?), are other owners happy with their purchase (are his other PhD students happy with his supervision?), etc. etc. Getting a really fast car only to crash it cause you can't drive it does mean much and getting a supervisor who after a year's time makes you want to quit your PhD doesn't mean much either. Most probably in both cases people are going to think less of you.

I think the most important thing is that you say that "I have started to dislike my professor too." that is a major problem and you should not pick a supervisor that you dislike. I do not mean that by being "homies" with supervisor; I mean about mutual respect and ability to work efficiently and with understanding about each other maybe "small quirks". (eg. My supervisor avoided setting up morning meeting with me because I am a night-person; it was fine, he even joked up about it at times "Next week I have X thing going on so we probably need to meet at 11.00. I know you'll just be out of bed but that is my only available time." That did not mean though I was not expected to be always punctuational for our meeting or having worked seriously on the projects at hand.

To recap: As you present things I would say "do not to work with this professor" but not because of his low h-index but because you say you do not like him and that his PhD students seem not to take good positions (low after-sales value :) ).

You mention that US institution are out of the equation effectively; "fine". Have you thought of PhD programmes in Europe? Some small, not too famous but reputable universities in EU can be stepping stones for a post-doc in US (Given you do excellent work at your PhD obviously).

• 17 picking a car based on his horse-power — or perhaps more accurately, on the number of times the car is mentioned on TV. –  JeffE Commented Mar 1, 2014 at 19:59
• Above comment is painfully correct. There are many excellent research opportunities with low h-index, and similarly, there are many "high h" positions that won't move you forward in the ways you hope. –  meawoppl Commented Mar 1, 2014 at 23:21

Having a low h-index doesn't mean that your professor is a poor scientist, in the same way that having a high h-index doesn't guarantee he/she is a good one. The primary reason is that the h-index is bounded from above by the total number of publications, so people who have entered the field recently have a lower h-index than those that have been working there for decades, simply because the former haven't had so much time to publish enough papers. Additionally, the h-index only cares about a minimum number of citations per publication, and it doesn't take into account the total number of citations per publication or the importance of those citations. For example, if I publish two papers in Science and then retire from academia, my h-index will never be higher than 2, even if those two papers are completely revolutionary and get cited a kazillion times by the biggest guns in the field. In contrast, if I publish 20 papers reporting trivial and mundane results in North Dakota Community College Engineering Bulletin that only get cited by a bunch of my colleagues in a seventh-rate journal, I can potentially get my h-index up to 20.

A better way of deciding if you want to work with this person is to spend an afternoon reading through some of his recent work, and then to ask yourself: Does this person's work look interesting enough that I want to spend the next several years talking to him every day? . Or If I was already a professor, would I advise my own students to go get a PhD under this guy? . Or, if in doubt, ask these questions to your current mentors, who probably will have a more informed opinion than you do.

I think the question of how good your advisor will be is of secondary importance here because the real question is will you do a PhD or not? PhD positions are not easy to come by (depending on the field of study.) This may be your only chance.

The role of advisor is of course important, especially when it comes to getting your post-doc positions. You need to ask whether you are confident enough in your own abilities to write notable papers that are going to compensate for the shortcomings of the advisor. Have you discussed with him the projects that he will want you to work on? The biggest danger is that he will want you to do something that you are not inspired by. If you like the projects he proposes and feel confident that you can do well even if your advisor's help is limited then you should go for it.

At least you will still be working part time so you have a backup. Why not give it a try and be prepared to drop out after one year if it does not look promising (but don't tell the prof that obviously).

One more thing, if you do go for it try to have a more positive attitude. No advisor is perfect but they are usually on your side.

I had a similar dilemma when I decided to pursue the PhD. The rank of the program is important but the intersection of your advisor's work and your interests is the critical factor. If your research is not related to that of your advisor, he will not be able to offer insights to guide you along. You can get in-depth guidance about literature searches, literature reviews, and selecting and arguing a thesis from many outstanding reference books. Furthermore, your advisor cannot cover the breadth and depth of these reference books in the few short meetings that will be allotted to you. What you need is concise, trenchant insight that is relevant to the research.

To address your last question:

Should a PhD done with a professor whose work does not receive much citations and who publishes rarely ?

There are many considerations as pointed out by other answers. H-index is one measure that might help you understand, at a glance, things about a scholar, but given the seriousness of your situation (wanting to do a PhD) you should dig deeper. For instance, does the professor have a low H-index because he is new to the field (as mentioned by Koldito)? Or is the H-index low because his area is highly specialized, and quite small? These might be reasons for relaxing how important this metric is in making your decision.

If, on the other hand, his H-index is low because he does not publish often (e.g. he does not value publishing as a scholarly work), or because he publishes in venues with low impact, these might be good reasons for concern. Similarly, you note that others in his field who would be experts from the west would have an H-index > 50; if this professor isn't an expert in the field then I would consider that cause for legitimate concern too.

You asked a second question:

This might be my only chance for a PhD and I am not sure what to do. What are my chances in western academic system after this PhD ?

Your insight that his previous students don't tend to get good postdocs is something to consider, especially if other students in the university are able to secure quality postdoc positions. My personal experience has been that if you want to secure a position in the western academic system you need to do something there first (a degree, a postdoc, etc.), so making sure your PhD puts you on the path to achieving this sounds like it is important for your goals. I would encourage you to ask the adviser directly who he collaborates with and how you can get experience through the PhD working with scholars worldwide. Be explicit about your goals. If he decides he doesn't want to work with you because of this, then he probably isn't the right supervisor for you.

And a final note of advice, your social networks and institutional affiliations are more important when you are looking for that first academic job than your h-index. H-index is used more regularly for judging things like tenure, promotion, etc. In the western system, from my experience, you want people to know at a glance that you have credentials that are rigorous and prestigious. If doing a PhD with this professor won't put you on this track then you should seriously consider your other options. But dig deeper than the H-index to investigate this.

I have been working as a graduate advisor for many years and I am giving you suggestions based on that experience. I hope I don't sound overly critical, just a couple of things I usually tell incoming PhD students about their expectations of graduate school. This may be because I work mostly with undergraduates transitioning directly into graduate school, so often I have to play the antagonist in these discussions to challenge my students to think about their own plans for their own future. And to be realistic. So here goes:

Most importantly, I think that you should also consider the amount of effort you are willing to put into the PhD. Typically, PhD students are asked to commit full time to it, and though this varies with the discipline, my experience working with PhD students is that the more time they spend developing themselves as academics and masters of their field, the better they do professionally.

I am concerned that you do not like your mentor/professor much. Are there others on your committee (or academics you are considering to be on your committee) that you do prefer? It's not unusual to not see eye-to-eye with your mentor - it is unusual that you don't want to continue working/ knowing him after your graduate - but rather his connections. Typically, his word to his connections is what begins your immersion in his network - so you will have to be careful to either keep that disdain in check or work will not be fun and challenging (as it should be) and will end up being a chore and make you more frustrated - and isolated.

Finally, about rising in the ranks of academia. Being a part of the Western academic society is not the ultimate social status. Being a highly valued academic in your chosen field of work is.

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Students in our PhD programs are encouraged from day one to think of this experience as their first job in business academia—a training ground for a challenging and rewarding career generating rigorous, relevant research that influences practice.

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The PhD in Business Administration grounds students in the disciplinary theories and research methods that form the foundation of an academic career. Jointly administered by HBS and GSAS, the program has four areas of study: Accounting and Management , Marketing , Strategy , and Technology and Operations Management . All areas of study involve roughly two years of coursework culminating in a field exam. The remaining years of the program are spent conducting independent research, working on co-authored publications, and writing the dissertation. Students join these programs from a wide range of backgrounds, from consulting to engineering. Many applicants possess liberal arts degrees, as there is not a requirement to possess a business degree before joining the program

The PhD in Business Economics provides students the opportunity to study in both Harvard’s world-class Economics Department and Harvard Business School. Throughout the program, coursework includes exploration of microeconomic theory, macroeconomic theory, probability and statistics, and econometrics. While some students join the Business Economics program directly from undergraduate or masters programs, others have worked in economic consulting firms or as research assistants at universities or intergovernmental organizations.

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PhD student for Ni-based model catalysts for dry methane reforming (index no. 6313-02)

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The Paul Scherrer Institute PSI is the largest research institute for natural and engineering sciences within Switzerland. We perform cutting-edge research in the fields of future technologies, energy and climate, health innovation and fundamentals of nature. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether, PSI employs 2300 people.

For the Microscopy and Magnetism Group we are looking for a

PhD student for Ni-based model catalysts for dry methane reforming

Your tasks This project is an exciting opportunity to contribute to an international and interdisciplinary research effort in the field of heterogenous catalysis. Your working place will be at PSI Villigen, but you will closely collaborate with the groups of Stefan Vajda at J. Heyrovsky Institute in Prague and with the group of Jeroen von Bokhoven at ETH Zürich. The project will be carried out taking advantage of cutting-edge x-ray microscopy facilities at the Swiss Light Source and in situ electron microscopy facilities at ScopeM in Zürich.

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• Perform spectroscopic and structural characterization of Ni-based nanocatalysts for dry methane reforming using correlative in situ x-ray and electron microscopy over a wide pressure range
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• You have excellent communication and scientific writing skills  

Additional Information

We offer Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure.

For further information, please contact Dr Armin Kleibert, phone +41 56 310 55 27.

Please submit your application online by 4 October 2024 (including addresses of referees) for the position as a PhD student (index no. 6313-02).

Paul Scherrer Institute, Human Resources Management, Alina Rao, 5232 Villigen PSI, Switzerland, www.psi.ch

Work Location(s)

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