undergraduate research abstract examples

Writing an Abstract for Your Research Paper

Definition and Purpose of Abstracts

An abstract is a short summary of your (published or unpublished) research paper, usually about a paragraph (c. 6-7 sentences, 150-250 words) long. A well-written abstract serves multiple purposes:

• an abstract lets readers get the gist or essence of your paper or article quickly, in order to decide whether to read the full paper;
• an abstract prepares readers to follow the detailed information, analyses, and arguments in your full paper;
• and, later, an abstract helps readers remember key points from your paper.

It’s also worth remembering that search engines and bibliographic databases use abstracts, as well as the title, to identify key terms for indexing your published paper. So what you include in your abstract and in your title are crucial for helping other researchers find your paper or article.

If you are writing an abstract for a course paper, your professor may give you specific guidelines for what to include and how to organize your abstract. Similarly, academic journals often have specific requirements for abstracts. So in addition to following the advice on this page, you should be sure to look for and follow any guidelines from the course or journal you’re writing for.

The Contents of an Abstract

Abstracts contain most of the following kinds of information in brief form. The body of your paper will, of course, develop and explain these ideas much more fully. As you will see in the samples below, the proportion of your abstract that you devote to each kind of information—and the sequence of that information—will vary, depending on the nature and genre of the paper that you are summarizing in your abstract. And in some cases, some of this information is implied, rather than stated explicitly. The Publication Manual of the American Psychological Association , which is widely used in the social sciences, gives specific guidelines for what to include in the abstract for different kinds of papers—for empirical studies, literature reviews or meta-analyses, theoretical papers, methodological papers, and case studies.

Here are the typical kinds of information found in most abstracts:

• the context or background information for your research; the general topic under study; the specific topic of your research
• the central questions or statement of the problem your research addresses
• what’s already known about this question, what previous research has done or shown
• the main reason(s) , the exigency, the rationale , the goals for your research—Why is it important to address these questions? Are you, for example, examining a new topic? Why is that topic worth examining? Are you filling a gap in previous research? Applying new methods to take a fresh look at existing ideas or data? Resolving a dispute within the literature in your field? . . .
• your research and/or analytical methods
• your main findings , results , or arguments
• the significance or implications of your findings or arguments.

Your abstract should be intelligible on its own, without a reader’s having to read your entire paper. And in an abstract, you usually do not cite references—most of your abstract will describe what you have studied in your research and what you have found and what you argue in your paper. In the body of your paper, you will cite the specific literature that informs your research.

When to Write Your Abstract

Although you might be tempted to write your abstract first because it will appear as the very first part of your paper, it’s a good idea to wait to write your abstract until after you’ve drafted your full paper, so that you know what you’re summarizing.

What follows are some sample abstracts in published papers or articles, all written by faculty at UW-Madison who come from a variety of disciplines. We have annotated these samples to help you see the work that these authors are doing within their abstracts.

Choosing Verb Tenses within Your Abstract

The social science sample (Sample 1) below uses the present tense to describe general facts and interpretations that have been and are currently true, including the prevailing explanation for the social phenomenon under study. That abstract also uses the present tense to describe the methods, the findings, the arguments, and the implications of the findings from their new research study. The authors use the past tense to describe previous research.

The humanities sample (Sample 2) below uses the past tense to describe completed events in the past (the texts created in the pulp fiction industry in the 1970s and 80s) and uses the present tense to describe what is happening in those texts, to explain the significance or meaning of those texts, and to describe the arguments presented in the article.

The science samples (Samples 3 and 4) below use the past tense to describe what previous research studies have done and the research the authors have conducted, the methods they have followed, and what they have found. In their rationale or justification for their research (what remains to be done), they use the present tense. They also use the present tense to introduce their study (in Sample 3, “Here we report . . .”) and to explain the significance of their study (In Sample 3, This reprogramming . . . “provides a scalable cell source for. . .”).

Sample Abstract 1

From the social sciences.

Reporting new findings about the reasons for increasing economic homogamy among spouses

Gonalons-Pons, Pilar, and Christine R. Schwartz. “Trends in Economic Homogamy: Changes in Assortative Mating or the Division of Labor in Marriage?” Demography , vol. 54, no. 3, 2017, pp. 985-1005.

Sample Abstract 2

From the humanities.

Analyzing underground pulp fiction publications in Tanzania, this article makes an argument about the cultural significance of those publications

Emily Callaci. “Street Textuality: Socialism, Masculinity, and Urban Belonging in Tanzania’s Pulp Fiction Publishing Industry, 1975-1985.” Comparative Studies in Society and History , vol. 59, no. 1, 2017, pp. 183-210.

Sample Abstract/Summary 3

From the sciences.

Reporting a new method for reprogramming adult mouse fibroblasts into induced cardiac progenitor cells

Lalit, Pratik A., Max R. Salick, Daryl O. Nelson, Jayne M. Squirrell, Christina M. Shafer, Neel G. Patel, Imaan Saeed, Eric G. Schmuck, Yogananda S. Markandeya, Rachel Wong, Martin R. Lea, Kevin W. Eliceiri, Timothy A. Hacker, Wendy C. Crone, Michael Kyba, Daniel J. Garry, Ron Stewart, James A. Thomson, Karen M. Downs, Gary E. Lyons, and Timothy J. Kamp. “Lineage Reprogramming of Fibroblasts into Proliferative Induced Cardiac Progenitor Cells by Defined Factors.” Cell Stem Cell , vol. 18, 2016, pp. 354-367.

Sample Abstract 4, a Structured Abstract

Reporting results about the effectiveness of antibiotic therapy in managing acute bacterial sinusitis, from a rigorously controlled study

Note: This journal requires authors to organize their abstract into four specific sections, with strict word limits. Because the headings for this structured abstract are self-explanatory, we have chosen not to add annotations to this sample abstract.

Wald, Ellen R., David Nash, and Jens Eickhoff. “Effectiveness of Amoxicillin/Clavulanate Potassium in the Treatment of Acute Bacterial Sinusitis in Children.” Pediatrics , vol. 124, no. 1, 2009, pp. 9-15.

“OBJECTIVE: The role of antibiotic therapy in managing acute bacterial sinusitis (ABS) in children is controversial. The purpose of this study was to determine the effectiveness of high-dose amoxicillin/potassium clavulanate in the treatment of children diagnosed with ABS.

METHODS : This was a randomized, double-blind, placebo-controlled study. Children 1 to 10 years of age with a clinical presentation compatible with ABS were eligible for participation. Patients were stratified according to age (<6 or ≥6 years) and clinical severity and randomly assigned to receive either amoxicillin (90 mg/kg) with potassium clavulanate (6.4 mg/kg) or placebo. A symptom survey was performed on days 0, 1, 2, 3, 5, 7, 10, 20, and 30. Patients were examined on day 14. Children’s conditions were rated as cured, improved, or failed according to scoring rules.

RESULTS: Two thousand one hundred thirty-five children with respiratory complaints were screened for enrollment; 139 (6.5%) had ABS. Fifty-eight patients were enrolled, and 56 were randomly assigned. The mean age was 6630 months. Fifty (89%) patients presented with persistent symptoms, and 6 (11%) presented with nonpersistent symptoms. In 24 (43%) children, the illness was classified as mild, whereas in the remaining 32 (57%) children it was severe. Of the 28 children who received the antibiotic, 14 (50%) were cured, 4 (14%) were improved, 4(14%) experienced treatment failure, and 6 (21%) withdrew. Of the 28children who received placebo, 4 (14%) were cured, 5 (18%) improved, and 19 (68%) experienced treatment failure. Children receiving the antibiotic were more likely to be cured (50% vs 14%) and less likely to have treatment failure (14% vs 68%) than children receiving the placebo.

CONCLUSIONS : ABS is a common complication of viral upper respiratory infections. Amoxicillin/potassium clavulanate results in significantly more cures and fewer failures than placebo, according to parental report of time to resolution.” (9)

Some Excellent Advice about Writing Abstracts for Basic Science Research Papers, by Professor Adriano Aguzzi from the Institute of Neuropathology at the University of Zurich:

Academic and Professional Writing

This is an accordion element with a series of buttons that open and close related content panels.

Analysis Papers

Reading Poetry

A Short Guide to Close Reading for Literary Analysis

Using Literary Quotations

Play Reviews

Writing a Rhetorical Précis to Analyze Nonfiction Texts

Incorporating Interview Data

Grant Proposals

Planning and Writing a Grant Proposal: The Basics

Additional Resources for Grants and Proposal Writing

Job Materials and Application Essays

Writing Personal Statements for Ph.D. Programs

• Before you begin: useful tips for writing your essay
• Guided brainstorming exercises
• Get more help with your essay
• Frequently Asked Questions

Resume Writing Tips

CV Writing Tips

Cover Letters

Business Letters

Proposals and Dissertations

Resources for Proposal Writers

Resources for Dissertators

Research Papers

Planning and Writing Research Papers

Quoting and Paraphrasing

Writing Annotated Bibliographies

Creating Poster Presentations

Thank-You Notes

Advice for Students Writing Thank-You Notes to Donors

Reading for a Review

Critical Reviews

Writing a Review of Literature

Scientific Reports

Scientific Report Format

Sample Lab Assignment

Writing for the Web

Writing an Effective Blog Post

Writing for Social Media: A Guide for Academics

University of Missouri

• Bias Hotline: Report bias incidents

Undergraduate Research

• How to Write An Abstract

Think of your abstract or artist statement like a movie trailer: it should leave the reader eager to learn more but knowledgeable enough to grasp the scope of your work. Although abstracts and artist statements need to contain key information on your project, your title and summary should be understandable to a lay audience.

Please remember that you can seek assistance with any of your writing needs at the MU Writing Center . Their tutors work with students from all disciplines on a wide variety of documents. And they are specially trained to use the Abstract Review Rubric that will be used on the abstracts reviewed at the Spring Forum.

Types of Research Summaries

Students should submit artist statements as their abstracts.  Artist statements should introduce to the art, performance, or creative work and include information on media and methods in creating the pieces.  The statements should also include a description of the inspiration for the work, the meaning the work signifies to the artist, the artistic influences, and any unique methods used to create the pieces.  Students are encouraged to explain the connections of the work with their inspirations or themes.  The statements should be specific to the work presented and not a general statements about the students’ artistic philosophies and approaches.  Effective artist statements should provide the viewer with information to better understand the work of the artists.  If presentations are based on previous performances, then students may include reflections on the performance experiences and audience reactions.

Abstracts should describe the nature of the project or piece (ex:  architectural images used for a charrette, fashion plates, advertising campaign story boards) and its intended purpose.  Students should describe the project or problem that they addressed and limitations and challenges that impact the design process.  Students may wish to include research conducted to provide context for the project and inform the design process. A description of the clients/end users may be included.  Information on inspirations, motivations, and influences may also be included as appropriate to the discipline and project.  A description of the project outcome should be included.

Abstracts should include a short introduction or background to put the research into context; purpose of the research project; a problem statement or thesis; a brief description of materials, methods, or subjects (as appropriate for the discipline); results and analysis; conclusions and implications; and recommendations.  For research projects still in progress at the time of abstract submission, students may opt to indicate that results and conclusions will be presented [at the Forum].

Tips for writing a clear and concise abstract

The title of your abstract/statement/poster should include some language that the lay person can understand.   When someone reads your title they should have SOME idea of the nature of your work and your discipline.

Ask a peer unfamiliar with your research to read your abstract. If they’re confused by it, others will be too.

Keep it short and sweet.

• Interesting eye-catching title
• Introduction: 1-3 sentences
• What you did: 1 sentence
• Why you did it: 1 sentence
• How you did it: 1 sentence
• Results or when they are expected: 2 sentences
• Conclusion: 1-3 sentences

Ideas to Address:

• The big picture your project helps tackle
• The problem motivating your work on this particular project
• General methods you used
• Results and/or conclusions
• The next steps for the project

Things to Avoid:

• A long and confusing title
• Jargon or complicated industry terms
• Long description of methods/procedures
• Exaggerating your results
• Exceeding the allowable word limit
• Forgetting to tell people why to care
• References that keep the abstract from being a “stand alone” document
• Being boring, confusing, or unintelligible!

Artist Statement

The artist statement should be an introduction to the art and include information on media and methods in creating the piece(s).  It should include a description of the inspiration for the work, what the work signifies to the artist, the artistic influences, and any unique methods used to create the work.  Students are encouraged to explain the connections of the work with their inspiration or theme.  The artist statement (up to 300 words) should be written in plain language to invite viewers to learn more about the artist’s work and make their own interpretations.  The statement should be specific to the piece(s) that will be on display, and not a general statement about the student’s artistic philosophy and approach.  An effective artist statement should provide the viewer with information to better understand and experience viewing the work on display.

Research/Applied Design Abstract

The project abstract (up to 300 words) should describe the nature of the project or piece (ex:  architectural images used for a charrette, fashion plates, small scale model of a theater set) and its intended purpose.  Students should describe the project or problem that was addressed and limitations and challenges that impact the design process.  Students may wish to include research conducted to provide context for the project and inform the design process. A description of the clients/end users may be included.  Information on inspirations, motivations, and influences may also be included as appropriate to the discipline and project.

Key Considerations

• What is the problem/ big picture that your project helps to address?
• What is the appropriate background to put your project into context? What do we know? What don’t we know? (informed rationale)
• What is YOUR project? What are you seeking to answer?
• How do you DO your research? What kind of data do you collect?  How do you collect it?
• What is the experimental design? Number of subjects or tests run? (quantify if you can!)
• Provide some data (not raw, but analyzed)
• What have you found? What are your results? How do you KNOW this – how did you analyze this?
• What does this mean?
• What are the next steps? What don’t we know still?
• How does this relate (again) to the bigger picture. Who should care and why?  (what is your audience?)

More Resources

• Abstract Writing Presentation from University of Illinois – Chicago
• Sample Abstracts
• A 10-Step Guide to Make Your Research Paper More Effective
• Your Artist Statement: Explaining the Unexplainable
• How to Write an Artist Statement

Here is the Forum Abstract Review Rubric for you and your mentor to use when writing your abstract to submit to the Spring Research & Creative Achievements Forum.

Let your curiosity lead the way:

Apply Today

• Arts & Sciences
• Graduate Studies in A&S

Writing an Abstract

What is an abstract.

An abstract is a summary of your paper and/or research project. It should be single-spaced, one paragraph, and approximately 250-300 words. It is NOT an introduction to your paper; rather, it should highlight your major points, explain why your work is important, describe how you researched your problem, and offer your conclusions.

How do I prepare an abstract?

An abstract should be clear and concise, without any grammatical mistakes or typographical errors. You may wish to have it reviewed by the  Writing Center , who are  happy to work with you on your abstract and are available via  appointments , as well as a writing instructor, tutor, or other writing specialist. 

For the purposes of the symposium, the wording of an abstract should be understandable to a well-read, interdisciplinary audience. Specialized terms should be either defined or avoided.

A successful abstract addresses the following points:

• Problem:  What is the central problem or question you investigated?
• Purpose : Why is your study important? How it is different from other similar investigations? Why we should care about your project?
• Methods : What are the important methods you used to perform your research?
• Results : What are the major results of the research project? (You do not have to detail all of the results, highlight only the major ones.)
• Interpretation : How do your results relate back to your central problem?
• Implications : Why are your results important? What can we learn from them?

It should not include any charts, tables, figures, footnotes, references or other supporting information.

Finally, please note that your abstract  must have the approval of your research mentor or advisor.

Samples of Abstracts

Browse through past volumes of WUSHTA and WUURD available via  WashU Open Scholarship  to view samples of abstracts in all disciplines, or take a look at the samples below:

Sample abstract: Social Sciences

Sample abstract: Natural Sciences

Sample abstract: Humanities

• Skip to main navigation
• Skip to main content

College Center for Research and Fellowships

• Our Students, Their Stories
• Research Scholars
• For Students
• Research Grant Programs
• 2024 Undergraduate Research Symposium: Honoring Mentors

Undergraduate Research Symposium: Abstract Writing

• Undergraduate Research Symposium: Research Resource Expo
• Undergraduate Research Symposium: Information for Presenters
• Undergraduate Research Symposium: FAQs
• Past Undergraduate Research Symposium Events
• National Fellowships
• Collegiate Honors & Scholars
• CCRF Seminars: The Common Year
• CCRF General Resources for Students
• CCRF Resources for Faculty & Staff
• Connect with Us

Search form

What is a research abstract?

An abstract is a concise summary of a larger research project. It should address all the major points of the project, providing an overview of the research topic, question, methods, results, and significance. The abstract is a snapshot that captures a reader's attention and--although it can stand alone as a representation of the project--invites readers to learn more by viewing your poster or attending your presentation.

What should a research abstract include?

You will likely want to include:

• Background, context, and purpose (the "big picture" in which your research fits)
• Your question, hypothesis, or goal
• The methods and research design you employed/are employing
• Results, products, outcomes (achieved or anticipated)
• Implications and significance: for your field, for future work, for your viewers

What is the format of a research abstract?

The length and format of research abstracts can vary depending on the requirements of a particular conference or journal.

For the UChicago Undergraduate Research Symposium, the requirements are :

• Include a short, descriptive title, capitalized in title case
• Make it only one paragraph
• Have no section breaks, footnotes, or illustrations
• Adhere to a limit of 300 words.
• Pitch your abstract to an educated non-specialist audience (minimize jargon, spell out acronyms)

Should I show my abstract to my mentor?

YES! It is very important that your faculty mentor review and approve your abstract. Your abstract will be publicly available, so you and your mentor should work together to ensure that the abstract presents your work appropriately and does not raise any intellectual-property concerns. Your mentor will need to approve your abstract in our application system before you can be accepted to present at the Undergraduate Research Symposium.

Will you host abstract writing workshops?

Yes! Abstract workshops are now concluded. View the slides and a recorded workshop in our Resource Library .

Where can I find examples of research abstracts?

You can review abstracts from major conferences or journals in your field to ascertain the conventions of your discipline. You can also review the abstracts from prior Undergraduate Research Symposia to see what your peers have written.

Where do I submit my abstract for the upcoming Undergraduate Research Symposium?

Access the online submission form here .

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 3. The Abstract
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

An abstract summarizes, usually in one paragraph of 300 words or less, the major aspects of the entire paper in a prescribed sequence that includes: 1) the overall purpose of the study and the research problem(s) you investigated; 2) the basic design of the study; 3) major findings or trends found as a result of your analysis; and, 4) a brief summary of your interpretations and conclusions.

Writing an Abstract. The Writing Center. Clarion University, 2009; Writing an Abstract for Your Research Paper. The Writing Center, University of Wisconsin, Madison; Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-first Century . Oxford, UK: Chandos Publishing, 2010;

Importance of a Good Abstract

Sometimes your professor will ask you to include an abstract, or general summary of your work, with your research paper. The abstract allows you to elaborate upon each major aspect of the paper and helps readers decide whether they want to read the rest of the paper. Therefore, enough key information [e.g., summary results, observations, trends, etc.] must be included to make the abstract useful to someone who may want to examine your work.

How do you know when you have enough information in your abstract? A simple rule-of-thumb is to imagine that you are another researcher doing a similar study. Then ask yourself: if your abstract was the only part of the paper you could access, would you be happy with the amount of information presented there? Does it tell the whole story about your study? If the answer is "no" then the abstract likely needs to be revised.

Farkas, David K. “A Scheme for Understanding and Writing Summaries.” Technical Communication 67 (August 2020): 45-60;  How to Write a Research Abstract. Office of Undergraduate Research. University of Kentucky; Staiger, David L. “What Today’s Students Need to Know about Writing Abstracts.” International Journal of Business Communication January 3 (1966): 29-33; Swales, John M. and Christine B. Feak. Abstracts and the Writing of Abstracts . Ann Arbor, MI: University of Michigan Press, 2009.

Structure and Writing Style

I.  Types of Abstracts

To begin, you need to determine which type of abstract you should include with your paper. There are four general types.

Critical Abstract A critical abstract provides, in addition to describing main findings and information, a judgment or comment about the study’s validity, reliability, or completeness. The researcher evaluates the paper and often compares it with other works on the same subject. Critical abstracts are generally 400-500 words in length due to the additional interpretive commentary. These types of abstracts are used infrequently.

Descriptive Abstract A descriptive abstract indicates the type of information found in the work. It makes no judgments about the work, nor does it provide results or conclusions of the research. It does incorporate key words found in the text and may include the purpose, methods, and scope of the research. Essentially, the descriptive abstract only describes the work being summarized. Some researchers consider it an outline of the work, rather than a summary. Descriptive abstracts are usually very short, 100 words or less. Informative Abstract The majority of abstracts are informative. While they still do not critique or evaluate a work, they do more than describe it. A good informative abstract acts as a surrogate for the work itself. That is, the researcher presents and explains all the main arguments and the important results and evidence in the paper. An informative abstract includes the information that can be found in a descriptive abstract [purpose, methods, scope] but it also includes the results and conclusions of the research and the recommendations of the author. The length varies according to discipline, but an informative abstract is usually no more than 300 words in length.

Highlight Abstract A highlight abstract is specifically written to attract the reader’s attention to the study. No pretense is made of there being either a balanced or complete picture of the paper and, in fact, incomplete and leading remarks may be used to spark the reader’s interest. In that a highlight abstract cannot stand independent of its associated article, it is not a true abstract and, therefore, rarely used in academic writing.

II.  Writing Style

Use the active voice when possible , but note that much of your abstract may require passive sentence constructions. Regardless, write your abstract using concise, but complete, sentences. Get to the point quickly and always use the past tense because you are reporting on a study that has been completed.

Abstracts should be formatted as a single paragraph in a block format and with no paragraph indentations. In most cases, the abstract page immediately follows the title page. Do not number the page. Rules set forth in writing manual vary but, in general, you should center the word "Abstract" at the top of the page with double spacing between the heading and the abstract. The final sentences of an abstract concisely summarize your study’s conclusions, implications, or applications to practice and, if appropriate, can be followed by a statement about the need for additional research revealed from the findings.

Composing Your Abstract

Although it is the first section of your paper, the abstract should be written last since it will summarize the contents of your entire paper. A good strategy to begin composing your abstract is to take whole sentences or key phrases from each section of the paper and put them in a sequence that summarizes the contents. Then revise or add connecting phrases or words to make the narrative flow clearly and smoothly. Note that statistical findings should be reported parenthetically [i.e., written in parentheses].

Before handing in your final paper, check to make sure that the information in the abstract completely agrees with what you have written in the paper. Think of the abstract as a sequential set of complete sentences describing the most crucial information using the fewest necessary words. The abstract SHOULD NOT contain:

• A catchy introductory phrase, provocative quote, or other device to grab the reader's attention,
• Lengthy background or contextual information,
• Redundant phrases, unnecessary adverbs and adjectives, and repetitive information;
• Acronyms or abbreviations,
• References to other literature [say something like, "current research shows that..." or "studies have indicated..."],
• Using ellipticals [i.e., ending with "..."] or incomplete sentences,
• Jargon or terms that may be confusing to the reader,
• Citations to other works, and
• Any sort of image, illustration, figure, or table, or references to them.

Abstract. Writing Center. University of Kansas; Abstract. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Abstracts. The Writing Center. University of North Carolina; Borko, Harold and Seymour Chatman. "Criteria for Acceptable Abstracts: A Survey of Abstracters' Instructions." American Documentation 14 (April 1963): 149-160; Abstracts. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Hartley, James and Lucy Betts. "Common Weaknesses in Traditional Abstracts in the Social Sciences." Journal of the American Society for Information Science and Technology 60 (October 2009): 2010-2018; Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-first Century. Oxford, UK: Chandos Publishing, 2010; Procter, Margaret. The Abstract. University College Writing Centre. University of Toronto; Riordan, Laura. “Mastering the Art of Abstracts.” The Journal of the American Osteopathic Association 115 (January 2015 ): 41-47; Writing Report Abstracts. The Writing Lab and The OWL. Purdue University; Writing Abstracts. Writing Tutorial Services, Center for Innovative Teaching and Learning. Indiana University; Koltay, Tibor. Abstracts and Abstracting: A Genre and Set of Skills for the Twenty-First Century . Oxford, UK: 2010; Writing an Abstract for Your Research Paper. The Writing Center, University of Wisconsin, Madison.

Writing Tip

Never Cite Just the Abstract!

Citing to just a journal article's abstract does not confirm for the reader that you have conducted a thorough or reliable review of the literature. If the full-text is not available, go to the USC Libraries main page and enter the title of the article [NOT the title of the journal]. If the Libraries have a subscription to the journal, the article should appear with a link to the full-text or to the journal publisher page where you can get the article. If the article does not appear, try searching Google Scholar using the link on the USC Libraries main page. If you still can't find the article after doing this, contact a librarian or you can request it from our free i nterlibrary loan and document delivery service .

• << Previous: Research Process Video Series
• Next: Executive Summary >>
• Last Updated: May 15, 2024 9:53 AM
• URL: https://libguides.usc.edu/writingguide

• Admission and Aid
• Student Life

Undergraduate Research and Creative Scholarship

Site navigation.

• About OUR@UM
• Getting Started
• Student Resources
• Presenting & Publishing
• Faculty Mentor Award
• Resources for Faculty Mentors
• Appointments

UMCUR Section Sidebar Navigation

• Participation Details
• Important Dates
• Workshops & Resources
• Judges & Volunteers

Sample Abstracts

Sample physical and life sciences abstract.

Do Voles Select Dense Vegetation for Movement Pathways at the Microhabitat Level? Biological Sciences The relationship between habitat use by voles (Rodentia: Microtus) and the density of vegetative cover was studied to determine if voles select forage areas at the microhabitat level.  Using live traps, I trapped, powdered, and released voles at 10 sites.  At each trap site, I analyzed the type and height of the vegetation in the immediate area.  Using a black light, I followed the trails left by powdered voles through the vegetation.  I mapped the trails using a compass to ascertain the tortuosity or amount the trail twisted and turned, and visually checked the trails to determine the obstruction of the movement path by vegetation.  I also checked vegetative obstruction on 4 random paths near the actual trail, to compare the cover on the trail with other nearby alternative pathways.  There was not a statistically significant difference between the amount of cover on a vole trail and the cover off to the sides of the trail when completely covered; there was a significant difference between on and off the trail when the path was completely open.  These results indicate that voles are selectively avoiding bare areas, while not choosing among dense patches at a fine microhabitat scale.

Sample Social Science Abstract

Traditional Healers and the HIV Crisis in Africa:  Toward an Integrated Approach Anthropology The HIV virus is currently destroying all facets of African life. It, therefore, is imperative that a new holistic form of health education and accessible treatment be implemented in African public health policy which improves dissemination of prevention and treatment programs while maintaining the cultural infrastructure. Drawing on government and NGO reports, as well as other documentary sources, this paper examines the nature of current efforts and the state of health care practices in Africa. I review access to modern health care and factors that inhibit local utilization of these resources, as well as traditional African beliefs about medicine, disease, and healthcare. This review indicates that a collaboration of western and traditional medical care and philosophy can help slow the spread of HIV in Africa. This paper encourages the acceptance and financial support of traditional health practitioners in this effort owing to their accessibility and affordability and their cultural compatibility with the community.

Sample Humanities Abstract

Echoes from the Underground European and American Literature Friedrich Nietzsche notably referred to the Russian novelist Fyodor Dostoevsky as “the only psychologist from whom I have anything to learn.” Dostoevsky’s ability to encapsulate the darkest and most twisted depths of the human psyche within his characters has had a profound impact on those writers operating on the periphery of society. Through research on his writing style, biography, and a close reading of his novel Notes from the Underground I am exploring the impact of his most famous outcast, the Underground Man, on counterculture writers in America during the great subculture upsurge of the 1950s and 60s. Ken Kesey, Allen Ginsberg, and Jack Kerouac employ both the universal themes expressed by the Underground Man as well as more specific stylistic and textual similarities. Through my research, I have drawn parallels between these three writers with respect to their literary works as well as the impact of both their personal lives and the worlds that they inhabit. The paper affirms that Dostoevsky has had a profound influence on the geography of the Underground and that this literary topos has had an impact on the writers who continue to inhabit that space.

Sample Creative Writing Abstract

Passersby Creative Writing Richard Hugo wrote in his book of essays, The Triggering Town , that “knowing can be a limiting thing.” His experiences, however brief, in many of the small towns that pepper Montana’s landscape served as the inspiration to much of his poetry, and his observations came to reveal more of the poet than of the triggering subject. For Hugo, the less he knew of a place, the more he could imagine. My project, “Passersby,” is a short collection of poems and black and white photographs that explore this notion of knowledge and imagination. The place is the triggering subject in “Passersby” and will take the audience or viewer to a variety of national and international locations, from Rome and Paris to Beaver, Utah, and the Oregon Coast, and from there, into an exploration of experience and imagination relished by the poet. Hugo believed that as a writer “you owe reality nothing and the truth about your feelings everything.” While reality will play a role in “Passersby,” this work aims to blur the lines between knowing and imagination in order, perhaps, to find a truer place for the poet.

Sample Visual and Performing Arts Abstract/Artist Statement

The Integration of Historic Periods in Costume Design   Theatre As productions turn away from resurrecting museum pieces, integrating costumes from two different historical periods has become more popular. This research project focuses on what makes costume integration successful. A successful integration must be visually compelling, but still, give characters depth and tell the story of the play. By examining several Shakespearean theatre productions, I have pinpointed the key aspects of each costume integration that successfully assist the production. While my own experiences have merged Elizabethan with the 1950s, other designers have merged Elizabethan with contemporary and even a rock concert theme. By analyzing a variety of productions, connecting threads helped establish “rules” for designers.

Through this research, I have established common guidelines for integrating two periods of costume history while still maintaining a strong design that helps tell a story. One method establishes the silhouette of one period while combining the details, such as fabric and accessories, of another period, creating an equal representation of the two. A second option creates a world blended equally of the two periods, in which the design becomes timeless and unique to the world of the play. A third option assigns opposing groups to two different periods, establishing visual conflict. Many more may exist, but the overall key to costume integration is to define how each period is represented. When no rules exist, there is no cohesion of ideas and the audience loses sight of character, story, and concept. Costumes help tell a story, and without guidance, that story is lost.

Sample Journalism Abstracts

International Headlines 3.0: Exploring Youth-Centered Innovation in Global News Delivery Traditional news media must innovate to maintain their ability to inform contemporary audiences. This research project analyzes innovative news outlets that have the potential to draw young audiences to follow global current events. On February 8, 2011, a Pew Research Center Poll found that 52 percent of Americans reported having heard little or nothing about the anti-government protests in Egypt. Egyptians had been protesting for nearly two weeks when this poll was conducted. The lack of knowledge about the protests was not a result of scarce media attention. In the United States, most mainstream TV news sources (CNN, FOX, MSNBC, ABC) ran headline stories on the protests by January 26, one day after the protests began. Sparked by an assignment in International Reporting J450 class, we selected 20 innovative news outlets to investigate whether they are likely to overcome the apparent disinterest of Americans, particularly the youth, in foreign news. Besides testing those news outlets for one week, we explored the coverage and financing of these outlets, and we are communicating with their editors and writers to best understand how and why they publish as they do. We will evaluate them, following a rubric, and categorize them based on their usefulness and effectiveness.

Launch UM virtual tour.

Office of Undergraduate Research

• Office of Undergraduate Research FAQ's
• URSA Engage
• Resources for Students
• Resources for Faculty
• Engaging in Research
• Spring Poster Symposium (SPS)
• Ecampus SPS Videos
• Earn Money by Participating in Research Studies
• Transcript Notation
• Student Publications

How to Write an Abstract

How to write an abstract for a conference, what is an abstract and why is it important, an abstract is a brief summary of your research or creative project, usually about a paragraph long (250-350 words), and is written when you are ready to present your research or included in a thesis or research publication..

For additional support in writing your abstract, you can contact the Office of URSA at [email protected]  or schedule a time to meet with a Writing and Research Consultant at the OSU Writing Center 

Main Components of an Abstract: 

The opening sentences should summarize your topic and describe what researchers already know, with reference to the literature. 

A brief discussion that clearly states the purpose of your research or creative project. This should give general background information on your work and allow people from different fields to understand what you are talking about. Use verbs like investigate, analyze, test, etc. to describe how you began your work. 

In this section you will be discussing the ways in which your research was performed and the type of tools or methodological techniques you used to conduct your research. 

This is where you describe the main findings of your research study and what you have learned. Try to include only the most important findings of your research that will allow the reader to understand your conclusions. If you have not completed the project, talk about your anticipated results and what you expect the outcomes of the study to be. 

Significance

This is the final section of your abstract where you summarize the work performed. This is where you also discuss the relevance of your work and how it advances your field and the scientific field in general.

• Your word count for a conference may be limited, so make your abstract as clear and concise as possible.
• Organize it by using good transition words found on the lef so the information flows well.
• Have your abstract proofread and receive feedback from your supervisor, advisor, peers, writing center, or other professors from different disciplines. 
• Double-check on the guidelines for your abstract and adhere to any formatting or word count requirements.
• Do not include bibliographic references or footnotes. 
• Avoid the overuse of technical terms or jargon. 

Feeling stuck? Visit the OSU ScholarsArchive for more abstract examples related to your field

Contact Info

618 Kerr Administration Building Corvallis, OR 97331

541-737-5105

Sample abstracts

Reprocessing used nuclear fuel (UNF) is crucial to the completion of a closed fuel cycle and would reduce the volume of waste produced during nuclear power production. Pyroprocessing is a promising reprocessing technique as it offers pure forms of product recovery. A limiting issue with pyroprocessing, however, is the inability to monitor concentrations of chemical species inside the electrorefiner. As with many nuclear processes, safe guards and monitoring become increasingly important; therefore, development of real - time monitoring techniques for various chemical species may allow for commercialization of this recycling process [1 - 5]. The focus of the proposed research is to develop accurate diffusion coefficients for Yttrium, a fission product found in UNF, in molten salt conditions through Cyclic Voltammetry (CV). Quantification of the diffusion coefficient will allow current measurements from inside the melt to be directly related to species concentration. With the diffusion coefficients, in - situ CV would then facilitate real - time monitoring of chemical concentrations.

This project aims to analyze the social and cultural effects of the Iranian Revolution through primary source material and interviews with those directly affected by the revolution. Iran’s political seclusion and its animosity toward the West has limited the voices and perspectives available to an American audience. Moreover, the attitude of the West towards Iran since the revolution has been myopic and often marred by political perspectives. The objective of this project will be to bring those voices and stories to light, putting a greater focus on the experiences of individuals who lived through the Revolution. These stories will be presented in a digital medium (film and web) in order to bring these voices and perspectives to an American audience.

Undergraduate Research Center

Undergraduate research abstract books:.

Check out these searchable PDF abstracts  from our annual undergraduate research conference as this is a great way to see examples of undergraduate research taking place on campus and which faculty have supported undergraduate students in their labs and on their research projects.

2021 Abstract Book

2020 Abstract Book

2019 Abstract Book

2018 Abstract Book

2017 Abstract Book            

2016 Abstract Book          

2015 Abstract Book            

2014 Abstract Book

2013 Abstract Book

2012 Abstract Book  

2011 Abstract Book  

• Office of Undergraduate Research
• Current Students
• Online Only Students
• Faculty & Staff
• Parents & Family
• Alumni & Friends
• Community & Business
• Student Life
• Student Assistants
• Latest News
• What is Research
• Get Started
• First-Year Scholars Program
• Current Research Projects
• Involvement Opportunities
• Undergraduate Research Space
• About to Graduate?
• Find Undergraduate Researchers
• Request a Classroom Visit
• Presenting and Publishing
• Workshops and Training
• Office of Research

How to Write a Research Abstract

Explore the possibilities.

Tuesday October 13, 2020 11:00am - 12:00pm Microsoft Teams Virtual Workshop

An abstract is a shortened version of a research project and is typically required for conference submissions and manuscripts submitted for publication. This workshop is focused on how to write an effective research abstract. Particular emphasis will be on writing abstracts for the National Conference on Undergraduate Research (NCUR), which will be held virtually in April 2021.

Contact Info

Kennesaw Campus 1000 Chastain Road Kennesaw, GA 30144

Marietta Campus 1100 South Marietta Pkwy Marietta, GA 30060

Campus Maps

Phone 470-KSU-INFO (470-578-4636)

kennesaw.edu/info

Media Resources

Resources For

Related Links

• Financial Aid
• Degrees, Majors & Programs
• Job Opportunities
• Campus Security
• Global Education
• Sustainability
• Accessibility

470-KSU-INFO (470-578-4636)

© 2024 Kennesaw State University. All Rights Reserved.

• Privacy Statement
• Accreditation
• Emergency Information
• Report a Concern
• Open Records
• Human Trafficking Notice

Info for Morgan State University

• Future Students
• Current Students
• Faculty & Staff
• Parents & Families
• Alumni & Friends
• For the Media

Search Morgan State University

Commonly searched pages.

• Payment Plan
• Housing Application
• Comptroller
• Office of Undergraduate Research
• Spring Into Research Week III
• Award Recipients
• Schedule of Events
• Abstract Submission & Registration Information
• Rules for Abstract Submission
• Abstract Format/ Sample Abstract
• Guest Registration/ Abstract Submission
• Poster and Oral Presentation Guidelines
• Call for Judges
• About the Office
• MSU/PGCC Bridges Program (B2B)
• Research Internships
• The Leadership Alliance
• Research Ambassadors
• Post-baccalaureate Training Opportunities
• OUR Resources

ABSTRACT FORMAT/ SAMPLE ABSTRACT

Abstracts must be submitted as an  MS WORD document  only.

All WORD documents must be submitted with the following formatted name:           Last name_discipline_poster or oral          (Example: Brown_biology_poster)

All abstracts must meet formatting requirements (see sample). Abstracts that do not meet all formatting/submission guidelines will not be accepted.

Abstracts must not exceed 250 words, excluding title, authors, and institutions.

Abstracts should be typed, single space in Times New Roman, 11 point font.

Abstract title must be bold and typed in ALL CAPITAL LETTERS.

Authors should be listed as follows:     *Joan A. Doe, Thomas T. Smith, and Carl Jones More than one student presenter is allowed for poster presentations. Only one presenter is allowed for oral presentations. (*Indicates the student presenter).

List Department, Institution, City, State, Zip Code as follows: Department of Biology, Morgan State University, Baltimore, MD 21251.

Skip one line, indent (5) spaces, and begin typing the abstract.

Abstracts should contain the following: 1.  INTRODUCTION  outlining the significance of the research project. (For the arts categories, the introduction should state the problem/nature and significance of the topic.) 2. Statement of the  HYPOTHESIS  being tested or the  OBJECTIVE  for the research. 3. Brief statement of  RESEARCH METHODS  used. (For the arts, state the method/investigative strategy.) 4. Summary of the  RESULTS . 5. Statement of the  CONCLUSIONS . 6. A list of grants that support your abstract.

SAMPLE ABSTRACT

IDENTIFICATION OF GAMMA-2-MELANOCYTE STIMULATING HORMONE (ɣ-2-MSH) RESPONSIVE GENES IN MC3R TRANSFECTED BRAINSTEM CAD CELLS BY MICROARRAY ANALYSIS .  *Segun Bernard, Brian Redmond, James Wachira and Cleo Hughes Darden.  Morgan State University, Department of Biology, Baltimore, MD 21251.

          Melanocortins are peptide hormones that are derived from the precursor polypeptide pro-opiomelanocortin. They mediate their effects through a family) of five G-protein coupled receptors, the melanocortin receptors. Some studies have implicated other signaling pathways such as the PKC, MAP kinase, and the JAK/STAT pathways. Melanocortin receptors, melanocortin-3-receptor (MC3R) and melanocortin-4-receptor (MC4R), have been implicated in the pathophysiology of obesity, insulin resistance and salt-sensitive hypertension through gene knockout studies. In order to understand the molecular mechanisms involved in MC3R signaling, we treated MC3R/GFP and GFP control transfected cells with gamma-MSH and isolated total RNA for gene transcription analysis using oligonucleotide microarrays. Total RNA isolated from the two populations of harvested cells was amplified, labeled and co-hybridized to oligonucleotide microarrays. Eighty-eight genes were up-regulated and 91 genes were down-regulated with > 2 ratio and p-value of < 0.05. Several pathways were altered including signal transduction and G-protein coupled receptor protein signaling, among others. Quantitative PCR data indicate that protein tyrosine phosphatase and protein kinase nu genes are up-regulated as a result of MC3R activation by gamma-MSH. The information gathered from this study will enhance our knowledge of the molecular mechanisms involved in MC3R signaling because of its involvement in salt sensitive hypertension and cardiovascular function. (Supported by NIH/NCRR/RCMI/G12RR17581-05 and RCMI funded core facilities)

Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Welcome to the Purdue Online Writing Lab

Welcome to the Purdue OWL

This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.

Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.

The Online Writing Lab at Purdue University houses writing resources and instructional material, and we provide these as a free service of the Writing Lab at Purdue. Students, members of the community, and users worldwide will find information to assist with many writing projects. Teachers and trainers may use this material for in-class and out-of-class instruction.

The Purdue On-Campus Writing Lab and Purdue Online Writing Lab assist clients in their development as writers—no matter what their skill level—with on-campus consultations, online participation, and community engagement. The Purdue Writing Lab serves the Purdue, West Lafayette, campus and coordinates with local literacy initiatives. The Purdue OWL offers global support through online reference materials and services.

A Message From the Assistant Director of Content Development 

The Purdue OWL® is committed to supporting  students, instructors, and writers by offering a wide range of resources that are developed and revised with them in mind. To do this, the OWL team is always exploring possibilties for a better design, allowing accessibility and user experience to guide our process. As the OWL undergoes some changes, we welcome your feedback and suggestions by email at any time.

Please don't hesitate to contact us via our contact page  if you have any questions or comments.

All the best,

Social Media

Facebook twitter.

Socio-emotional experiences of primary school students: Relations to teachers’ underestimation, overestimation, or accurate judgment of their cognitive ability

• Open access
• Published: 15 May 2024

Cite this article

You have full access to this open access article

• Jessica Gnas 1 ,
• Julian Urban 1 , 2 ,
• Markus Daniel Feuchter 3 &
• Franzis Preckel 1  

Previous research revealed that students who are overestimated in their ability by their teachers experience school more positively than underestimated students. In the present study, we compared the socio-emotional experiences of N  = 1516 students whose cognitive abilities were overestimated, accurately judged, or underestimated by their teachers. We applied propensity score matching using students’ cognitive ability, gender, language, parental education, and teacher’s acquaintance with them as covariates for building the three student groups. Matching students on these variables, reduced the original sample size to subsamples with n 1  = 348, and n 2  = 312 with exact matching including classroom. We compared overestimated, accurately judged, or underestimated students in both matching samples in their socio-emotional profiles (comprised of academic self-concept, joy of learning, attitude towards school, willingness to make an effort, social integration, perceived class climate, and feeling of being accepted by the teacher) by linear discriminant analyses. Groups significantly differed in their profiles. Overestimated students had the most positive socio-emotional experiences of school, followed by accurately judged students. Underestimated students experienced school most negatively. Differences in experiences were most pronounced for the learning environment (medium to large effects for academic self-concept, joy of learning, and willingness to make an effort; negligible effect for attitude towards school) and less for the social environment (medium effects for feeling of being accepted by the teacher; negligible effects for social integration and perceived class climate).

Avoid common mistakes on your manuscript.

1 Theoretical background

1.1 introduction.

How students experience school influences their socio-emotional and personal development. A positive attitude towards school and good social integration at school foster students’ socio-emotional and personal growth (Aviles et al., 2006 ) and academic success (Lam et al., 2018 ). Teachers are heavily involved in students’ socio-emotional experiences of school (SEES); they are central reference persons for students and influence the class’s academic and social climate. Accordingly, students’ positive SEES are associated with teacher support and their relationship with the teacher (Aviles et al., 2006 ; Heller et al., 2012 ; Rucinski et al., 2018 ), teachers’ early childhood specialization (Nocita et al., 2020 ), and teachers’ classroom management skills (Korpershoek et al., 2016 ).

Several studies have investigated the relation between students’ SEES and whether they are over- or underestimated by their teachers. The results consistently show that students who are underestimated in their achievement are at a disadvantage compared to students who are overestimated. For example, underestimated students have a more negative self-concept, enjoy school less, and feel less supported by their teachers and peers (e.g., Rubie-Davies & Peterson, 2016 ; Urhahne, 2015 ). Most studies in this research field have focused on teacher judgments of students’ achievement. However, examining teacher judgments of students’ cognitive ability—rather than academic achievement—in relation to students’ SEES is important because teachers tend to make larger misjudgments for students’ cognitive abilities compared to their academic achievement (Machts et al., 2016 ; Südkamp et al., 2012 ). There is initial evidence supporting the idea that students’ SEES is related to their teachers’ judgments of their cognitive ability. A longitudinal study with primary school students by Baudson ( 2011 ) found that children who were underestimated by their teachers in terms of cognitive ability developed less positively in their academic self-concept, interest, and attitude towards school one year later compared to students who were overestimated. However, other factors could be responsible for the observed differences in the students’ development in this study and more research is needed with matched student samples. In addition, most studies in this area have only investigated students in Grade 4 and above. However, positive experiences in school are particularly relevant in the early school years, and they are a crucial starting point for further learning and socio-emotional development (Aviles et al., 2006 ). Therefore, there is a need for studies with younger students in their primary school years.

In the present study, we add to the literature by investigating a large sample of primary school students in Grades 1 to 4. We assessed multiple dimensions of students’ SEES and teacher judgments of students’ cognitive ability, and controlled for potential confounding variables by matching underestimated, accurately judged, and overestimated students on these variables using propensity score matching (PSM). Our findings may serve to heighten the awareness of possible positive and negative associations between teacher judgments of students’ abilities and students’ SEES.

1.2 Students’ socio-emotional experiences of school

Students’ SEES constitute mental representations of the school environment gained from their experiences. It is a collective term for a multitude of constructs related to the experience of school. De Fruyt et al. ( 2015 ) defined students’ SEES rather broadly as thoughts, feelings, and behaviors developed through learning experiences. Primi et al. ( 2021 ) proposed more specific skills covering socio-emotional functioning in adolescents (i.e., self-management, engaging with others, amity, negative emotion regulation, and open-mindedness). Rauer and Schuck ( 2003 ) defined SEES as the perception and evaluation of the school environment, one’s social relationships and integration, school- and learning-related climate, and one’s own competency. The different dimensions of students’ SEES can broadly be grouped into relationships (with other students and the teacher) and characteristics of the classroom and the school (Eder, 2018 ; Grewe, 2017 ). Similarly, Gnas et al. ( 2022a ) distinguished between the experience of the learning environment and the experience of the social environment . The experience of the learning environment includes students’ academic self-concept, their joy of learning, their attitude towards school, and their willingness to make an effort; the experience of the social environment includes students’ perceptions of their social integration, their feeling of being accepted by the teacher, and the class climate (see Rauer & Schuck, 2003 ; for definitions, see Table  1 ).

Several factors are associated with students’ SEES. First, girls experience school more positively than boys on average (Bergold et al., 2020 ; Likhanov et al., 2020 ; Van Rossem & Vermande, 2004 ). Secondly, positive SEES are related to a positive working atmosphere at school (e.g., high levels of teacher acquaintance with the student, existence of rules in the classroom, low performance goals and competitive pressure; Hofmann & Siebertz-Reckzeh, 2008 ; Johns, 2020 ). Thirdly, social support from peers or adults, such as parents, caregivers, or teachers, promotes positive SEES (Aviles et al., 2006 ). Since students spend a significant part of their lives in school, teachers play a particularly important role. Students’ SEES are associated with their relationship with the teacher (Heller et al., 2012 ; Rucinski et al., 2018 ), teachers’ classroom management (Korpershoek et al., 2016 ), and teachers’ specialization in early childhood education and care, which might be explained by the fact that they gain specialized knowledge and skills that enable them to interact with children in ways that are effective at supporting their future development (Nocita et al., 2020 ). Moreover, teacher judgments of students’ achievement or ability are related to students’ SEES (Wang et al., 2018 ).

1.3 Teacher judgments and students’ socio-emotional experiences of school

In the following, we introduce teacher judgments and their accuracy, and present findings on teacher judgments of students’ achievement and ability. Below, we summarize methods for studying over- and underestimation. We then review findings of studies comparing the SEES of students who are overestimated and underestimated by their teachers.

1.3.1 Teacher judgments of students’ achievement and ability

One central part of teachers’ professional competency is their diagnostic competency (Baumert & Kunter, 2011 ). It can be defined as the competency to correctly judge a student or task concerning a specific characteristic (Urhahne & Wijnia, 2021 ). In research, the term is used interchangeably with teacher judgment accuracy (TJA; Urhahne & Wijnia, 2021 ). Teachers high in this competency can judge task characteristics, such as the demands that certain learning tasks make for students, and student characteristics, such as their cognitive ability. Teachers who are low in judgment accuracy, to various extents, over- or underestimate student or task characteristics. Research distinguishes between relative and absolute TJA (Urhahne & Wijnia, 2021 ). Relative TJA concerns the relation (e.g., the correlation) between the teacher judgment of task or student characteristics and the actual characteristics of the task or student. Absolute TJA concerns the difference between the judged and actual task or student characteristic and allows consideration of both the magnitude and direction of judgment inaccuracies (i.e., over- and underestimation).

To date, most of the studies on TJA have focused on relative TJA concerning judgments of students’ academic achievement and cognitive ability Footnote 1 . Three meta-analyses examined TJA by correlating teacher judgments with students’ actual achievement or cognitive ability. The results demonstrate that teachers are more accurate in judging students’ achievement ( r  =.66 in 16 studies with 55 effect sizes, Hoge & Coladarci, 1989 ; r  =.63 in 75 studies with 73 effect sizes, Südkamp et al., 2012 ) than students’ cognitive ability ( r  =.43 in 33 studies with 106 effect sizes; Machts et al., 2016 ).

1.3.2 Studying overestimated versus underestimated students: methodological considerations

Absolute TJA allows researchers to investigate over- and underestimation. The literature reports two ways of operationalizing absolute TJA: residuals and the level component. Residuals are derived from regressing teacher judgments on student characteristics or vice versa. They display the share of variance in teacher judgments not explained by the actual student characteristic (e.g., Gentrup et al., 2020). The level component represents the difference between the values of teacher judgments and the actual student values (e.g., Zhou & Urhahne, 2013)

For studying over- and underestimation, the residuals or the level component can be used either as continuous variables or by using cut-offs to build groups of underestimated, overestimated, or accurately judged students. Both approaches have been used for the comparison of over- and underestimated students and their SEES (see Table 2 ). The comparison has been conducted by using TJA as a cut-off variable (e.g., binary: overestimated vs. underestimated students) or as a continuous variable (e.g., increasing residuals for decreasing TJA). Furthermore, for studying the relation of over- and underestimation with SEES, the studies either conducted group comparisons (e.g., comparing overestimated and underestimated students in their SEES) or analyzed relations (e.g., TJA served as a predictor for/was correlated with students’ SEES).

1.3.3 Overestimated versus underestimated students’ socio-emotional experiences of school

Table  3 summarizes the findings from studies on students’ SEES in relation to teachers’ over- and underestimation. Most of the findings are related to the experience of the learning environment, and only a few are related to the experience of the social environment. In five studies, students’ achievement was the characteristic judged by the teachers; in two studies, it was students’ cognitive or mathematical ability. Six studies reported actual TJA, and one study by Gniewosz and Watt ( 2017 ) reported students’ perception of TJA. Four studies took place in primary school, and three took place in secondary school. Half the studies did not include any control variables; the other half included only a few (mostly student achievement). Five studies were carried out cross-sectionally, whereas two studies investigated the long-term effect of over- and underestimation on students’ SEES using a longitudinal design. The samples varied between 144 and 1271 students; however, six of eight studies had samples with N  < 300. Finally and most importantly, most of the findings were significant and all were in favor of overestimated students. That is, overestimated students perceived their learning and social environment more positively than underestimated students.

Furthermore, Table  3 shows the effect sizes by which over- and underestimated students differed in various dimensions of the experience of the social and learning environment Footnote 2 . More specifically, effects for the experience of the social environment were either small or not significant. Small effects were found for perceived teacher support and behavior as well as perceived peer support (Rubie-Davies & Peterson, 2016 ; Stang & Urhahne, 2016 ; Urhahne, 2015 ); nonsignificant effects were found for changes in feelings of being accepted, social integration, and perceived class climate (Baudson, 2011 ). Effects for the experience of the learning environment were heterogeneous. Looking at the dimensions more closely, it becomes clear that the largest effects (medium to large effect sizes) were consistently present for academic self-concept and enjoyment (Baudson, 2011 ; Urhahne, 2015 ; Urhahne et al., 2010 , 2011 ). Furthermore, (consistently) small effects were found for changes in academic interest and attitude towards school, as well as students’ self-efficacy and test anxiety (Baudson, 2011 ; Rubie-Davies & Peterson, 2016 ; Urhahne, 2015 ; Urhahne et al., 2010 , 2011 ). The remaining dimensions were more heterogeneous: small, medium, and large effects were found for expectancy of success and attribution for success/failure (Urhahne et al., 2010 , 2011 ; Urhahne, 2015 ; Zhou & Urhahne, 2013 ); and nonsignificant, small, and medium effects were found for motivational variables (i.e., students’ learning motivation, learning goals, level of aspiration, and changes in utility and intrinsic values; Gniewosz & Watt, 2017 ; Rubie-Davies & Peterson, 2016 ; Urhahne, 2015 ; Urhahne et al., 2010 ; Urhahne et al., 2011 ). Altogether, overestimated and underestimated students descriptively differed more in their experience of the learning environment compared to their experience of the social environment. However, the findings mainly concern the TJA of academic achievement and there are more findings for the experience of the learning environment than for the social environment.

Relations between TJA and socio-emotional experiences of school are often explained by the relationship—or more specifically the interaction—between teachers and their students. In general, research shows that the relationship between teachers and their students is highly relevant for the socio-emotional experiences, development, and learning at school. The large-scale meta-study by Hattie ( 2009 ) with over 800 meta-analyses showed that, among various factors related to the teacher, the teacher-student interaction (e.g., allowing the experiences of the child to be recognized in the classroom, listening or empathy) had one of the strongest effects on student learning. Moreover, several recent meta-analyses showed small to moderate correlations between teacher-student relationships and student outcomes in (primary) school (e.g., motivation, well-being, engagement, learning participation, academic emotions; Emslander et al., 2023 ; Lei et al., 2018 ; Li & Xue, 2023 ).

In this context, relations between TJA and socio-emotional experiences of school are often explained by a teacher-student interaction model by Brophy and Good ( 1970 ) and Brophy ( 1983 ). In their 6-step model, the authors elaborated the processes of interaction between students and the teacher: (1) The teacher has different expectations regarding students’ performance or ability. (2) Consistent with these expectations, the teacher behaves differently toward his or her students. (3) The students react differently to the teacher because they have been treated differently by him or her. (4) If teacher behavior remains stable over time and if students do not change their behavior over time, it is likely to affect their experiences in the classroom, such as their self-concept, motivation, and social interactions. (5) These effects then confirm and reinforce teacher expectations, and students conform more to these expectations than they would have done otherwise. (6) Finally, this will lead to differences in students’ performance and other outcomes such as their SEES.

1.4 The present study

Previous research has shown that overestimated and underestimated students systematically differ in their SEES—always in favor of overestimated students (e.g., Urhahne, 2015 ). Most of this research deals with the TJA of academic achievement, and rarely with cognitive ability. However, especially in primary school, it is relevant to focus more on judging academic ability than achievement, since primary school students have relatively short learning histories in which they have received formalized instruction. Differences between overestimated and underestimated students seem to be stronger for the experience of the learning environment compared to the experience of the social environment. However, there are very few findings for the social environment. Thus, our research goal is to describe and compare students who are overestimated, underestimated, or accurately assessed by their teachers in terms of their cognitive abilities in their SEES in both the learning and social environment. Based on our literature review, our hypotheses were as follows:

Students’ SEES of school differ depending on the TJA of the teachers judging their cognitive ability, such that: (a) overestimated students experience school more positively than accurately judged and underestimated students, and (b) accurately judged students experience school more positively than underestimated students. That is, the three TJA groups differ in their experiences of the learning environment and of the social environment in school with overestimated students reporting the best experiences, followed by accurately judged students, and underestimated students reporting the worst experiences.

Differences in SEES between overestimated, accurately judged, and underestimated students are stronger for the experience of the learning environment than for the experience of the social environment.

For our research design, we used group comparisons (see Table 2 ) because the present study focuses on the description of different student groups. Group comparisons are more consistent with the person description approach whereas regression models are consistent with a variable-centered approach. Controlling the potential confounds in group comparisons can be achieved by matching students on relevant variables with statistical methods like PSM. The PSM approach has several strengths regarding our study aims: as overestimated, underestimated, and accurately judged students cannot be randomly assigned to these groups, PSM allows for a quasi-experimental design. Further, instead of comparing all students, PSM compares only those that are particularly similar, which reduces biased estimates. Finally, PSM overcomes potential weaknesses in the regression approach: First, a causal direction has to be chosen, which is only partially suitable for cross-sectional data and, moreover, does not fit the student description approach of the present study. Second, in a regression, only the variance in the dependent variable (e.g., SEES) that has not already been explained by other covariates can be explained by the predictor variables. That is, when including possible confounds in regression models as covariates, we would only explain partial variability in students’ SEES. This problem can be circumvented by PSM through matching students with regard to their teacher, classroom, cognitive ability, gender, parental background, and their teacher’s acquaintance with them. Matching for cognitive ability is crucial in order to only investigate relations with TJA and students’ SEES without potential cognitive ability differences between students. Further, several studies have found that teacher judgments vary by student gender (e.g., Bergold et al., 2021 ; Golle et al., 2023 ; Lavrijsen & Verschueren, 2020 ), level of parental education, and language background (Alvidrez & Weinstein, 1999 ; Baudson et al., 2016a ; Bergold et al., 2021 ; Gnas et al., 2022b ; Golle et al., 2023 ; Wollschläger, 2016 ), and teachers’ acquaintance with a student (Baudson et al., 2016a ; Gnas et al., 2022b ), which can all be matched with PSM. The matched groups were then compared using linear discriminant analyses, which allowed the investigation of students’ SEES as a multidimensional profile of their experiences of the learning environment and the social environment.

The sample originated from Project THINK, which is associated with the chair of giftedness research and education at the University of Trier, Germany. Within the project, an intelligence test for primary school students was developed (THINK 1–4; Baudson et al., 2016 ). The norm sample of THINK 1–4 included N  = 2850 students (Grades 1 to 4) from 209 classrooms in 70 German schools, as well as their teachers and parents. They constitute a quasi-representative cross-sectional sample of students at public primary schools in Germany. Data collection was conducted between September 2012 and February 2014. In addition to the assessment of cognitive abilities, the students, teachers, and parents filled out self-report questionnaires. Detailed information on data collection and the composition of the sample can be found in the THINK 1–4 manual (Baudson et al., 2016 ). Since the analyses of this study represent secondary analyses of the data, approval by the University of Trier IRB was not required.

After the data preparation (see below), the full sample included N  = 1516 students (12.7% in Grade 1, 21.8% in Grade 2, 19.0% in Grade 3, and 46.4% in Grade 4) from 32 schools and 119 classrooms. Of the students, 48.8% were girls and 51.2% were boys, with an average age of M  = 8.50 years ( SD  = 1.28).

In addition to the student sample, data from 119 teachers were used, of whom 89.4% were female and 10.6% were male, with an average age of M  = 41.88 years ( SD  = 11.56) and an average work experience of M  = 14.26 years ( SD  = 11.18). The teacher sample is representative in terms of age and gender distribution of the German teacher population (88.5% female primary school teachers; Statista, 2023a ; 68% teachers between 30 and 54 years; Statista, 2023b ).

2.2 Propensity score matching

To minimize the impact of relevant covariates (i.e., students’ cognitive ability, gender, language, parental education, and teacher acquaintance with them), we balanced these variables between the three TJA groups (i.e., overestimated, accurately judged, and underestimated students) using PSM (Rosenbaum & Rubin, 1983 , 1984 ). In the process, original group subsamples are compared and matched on relevant covariates (i.e., matching variables), reducing original group sample sizes. The goal is to create a selected constellation of grouped triplets showing high equivalence in a composite measure representing all matching variables. The propensity score (PS), a person’s conditional probability of belonging to one of the index groups given their individual set of matching variable scores, has proven to be a reliable composite measure for matching procedures (Gu & Rosenbaum, 1993 ; Li, 2013 ).

Our PSM procedure consisted of three steps. In step one, we estimated boosted regression PSs (cf., McCaffrey et al., 2004 ) representing individual conditional probabilities pertaining to Group 1 (overestimated students) for all study participants given their cognitive ability, gender, language, parental education, as well as a score indicating teacher acquaintance with the student. This was achieved by using the twang -package (version 2.5; Ridgeway et al., 2015 ) in R statistics (4.2.0; R Core Team, 2021 ).

In step two, we applied a matching algorithm called the MAny-Group-MAtching-algorithm (MAGMA; Urban et al., 2023 , 2024 ), which is uniquely capable of matching individuals from two or more groups. With previously estimated PSs as input, MAGMA iteratively matches triplets that have the lowest Mahalanobis distance scores regarding these PSs. This iterative procedure is repeated until all cases from the group with the lowest sample size (in this case Group 2, accurately judged students, n  = 286) are matched. Meanwhile, MAGMA extracts the iteration of matching, the respective distance, and a weight for inclusion. We conducted step two a second time, adding the restriction of matching only individuals in the same classroom (i.e., exact matching) to acknowledge potential class-level context influences (i.e., to control for the nested data structure of students grouped in the same classes and, accordingly, judged by the same teacher).

In step three, continuous and binary covariates ( ν  = 4; language was dismissed from the balance assessment, as it only has nominal measurement properties) formed the input for balance estimation. Using the extracted step-variable and starting with a minimum sample size of n  = 20 per group, we increased the sample size iteratively to find the optimal model considering balance and sample size. To do this, we compared the balance of all possible models with 20 \( \le \) n \( \le \) 286 per group. Balance estimation builds on pairwise standardized mean differences (Cohen’s d ), Pillai’s trace from a MANOVA across all covariates, and an average effect of absolute standardized mean differences (mean g ) extracted with meta-analytical techniques (Fisher et al., 2017 ; Viechtbauer, 2010 ). The number of effects (Cohen’s d ) smaller than a conventionally small effect (i.e., d < 0.20; Cohen, 1988 ) served as our main criteria for judging the models. For Pillai’s trace and mean g , smaller values indicate a better balance.

The PSM process was conducted for both matching variants—(1) matching without restrictions and (2) exact matching including classroom. We applied the R packages psych (version 2.2.9.; Revelle, 2022 ) and robumeta (version 2.0; Fisher et al., 2017 ) for balance estimation.

2.2.1 Matched sample 1

In the first matched sample, we did not consider the nested data structure resulting from classroom membership (i.e., matching without restrictions model). Matched Sample 1 included n  = 348 students (13.8% in Grade 1, 26.4% in Grade 2, 16.4% in Grade 3, and 43.4% in Grade 4) from 32 schools and 109 classrooms. Of the students, 53.3% were girls and 49.7% were boys with an average age of M  = 8.44 years ( SD  = 1.28).

2.2.2 Matched sample 2

In the second matched sample, we considered the nested data structure (i.e., exact matching including classroom). Matched Sample 2 included n  = 312 students (10.6% in Grade 1, 21.2% in Grade 2, 21.2% in Grade 3, and 47.1% in Grade 4) from 28 schools and 69 classrooms. Of the students, 56.4% were girls and 43.6% were boys with an average age of M  = 8.60 years ( SD  = 1.24).

2.3 Measures

2.3.1 socio-emotional experiences of school.

Students’ SEES were measured by the FEESS-K (Baudson & Preckel, 2015 ) which comprises a short-version of the FEESS (Fragebogen zur Erfassung emotionaler und sozialer Schulerfahrungen von Grundschulkindern; in English ‘Questionnaire for the Assessment of Emotional and Social School Experiences of Primary School Children’) by Rauer and Schuck ( 2003 , 2004 ). The validity of the FEESS has been demonstrated in Baudson et al. ( 2016 ), Rauer and Schuck ( 2003 , 2004 ), Scherrer et al. ( 2016 ), and Schmidt et al. ( 2017 ). The validity of the FEESS-K was shown in Gnas et al. ( 2022a ).

Experiences of the learning environment were assessed with the following scales: academic self-concept (e.g., ‘I do well in school’), joy of learning (e.g., ‘I enjoy learning’), attitude towards school (e.g., ‘I like to go to school’), and willingness to make an effort (e.g., ‘I do my best in school’). Experiences of the social environment were assessed with the following scales: social integration (e.g., ‘My classmates are nice to me’), class climate (e.g., ‘In the class, we all stick together’), and feeling of being accepted (e.g., ‘My teachers have time for me’). Each scale had 3 items, which were answered on a 3-point visual scale with a laughing smiley (= 2), a neutral smiley (= 1), and a sad smiley (‘frowny face’; = 0). Additionally, the scale points were verbally anchored as ‘yes, that’s actually always true’, ‘that’s sometimes true, sometimes not’, and ‘no, that’s not quite true’.

2.3.2 Teacher judgments of cognitive ability

The scale for measuring teacher judgments of students’ cognitive ability was developed within the THINK project. The validity of the scales has been demonstrated in prior studies (Baudson et al., 2016a ; Bergold et al., 2021 ; Gnas et al., 2022b ). Teachers judged their students’ cognitive ability on a rating scale with six items which describe student behaviors as indicators of general cognitive ability. The items are translated from the original German wording, for example: ’understands new learning content quickly’, ‘can remember most things the first time’, ‘recognizes connections very quickly’. The items were rated on a six-point Likert scale (1 = not correct at all, 6 = fully correct).

2.3.3 Teacher judgment accuracy

Absolute TJA was measured by the z -standardized residuals of the regression of students’ cognitive ability (see below) on teachers’ judgment of students’ cognitive ability. While some other studies on TJA calculated the regression in the opposite direction (e.g., Gentrup et al., 2020 ), the regression in this study was calculated in the direction described because it corresponds to the content of the construct TJA (i.e., how predictive are teachers’ judgments of students’ cognitive ability of students’ actual cognitive ability?). This further ensured the comparability of our findings with previous studies of TJA using the same dataset (Gnas et al., 2022a , b ; Wollschläger, 2016 ). A residual of 0 corresponds to an accurate judgment, residuals > 0 indicate underestimation (i.e., higher cognitive ability than predicted by the teacher judgment) and residuals < 0 indicate overestimation (i.e., lower cognitive ability than predicted by the teacher judgment).

2.3.4 Cognitive ability

Students’ cognitive ability was measured by the THINK 1–4 (Baudson et al., 2016 ). The test estimates the general cognitive ability of children in Grades 1 to 4 and consists of 36 items. There are three subdimensions: figural reasoning (e.g., recognizing and applying regularities in graphic figures; three subscales), verbal reasoning (e.g., selecting an image that matches a word; two subscales) and numerical reasoning (e.g., completing incomplete number series; three subscales). The internal consistency in the norm sample was α  = 0.77 to 0.82 (depending on the grade level). It is standardized and provides an IQ score ( M  = 100, SD  = 15) for overall test performance. The factor structure could be confirmed by confirmatory factor analyses. Moreover, the test has good internal validity (strong correlation with other intelligence tests, e.g., Wechsler Intelligence Scale for Children, Petermann, F. & Petermann, U., 2011 ) and criterion-related validity (e.g., positive relations to school grades and their development).

2.3.5 Language and parental educational attainment

Students’ native language was assessed in the parents’ questionnaire, differentiating between three categories: ‘only German’, ‘German and another language’, and ‘only another language’. Furthermore, parents’ highest educational attainment was assessed with 1 = ‘no graduation from secondary school’, 2 = ‘graduated from lowest secondary level’, 3 = ‘graduated from intermediate secondary level’, 4 = ‘graduated from highest secondary level’, 5 = ‘degree in tertiary education’, 6 = ‘doctoral degree.’ Only the highest educational attainment within each parent couple was reported.

2.3.6 Teacher acquaintance with the student

Teacher acquaintance with the student was measured in the teacher questionnaire by a single item (‘How well do you know the child?’) on a five-point Likert scale (1 = virtually not, 5 = very well).

2.3.7 Gender

The students provided information on their gender during the testing process. It was collected binarily (1 = girl, 2 = boy).

2.4 Data analyses

2.4.1 data preparation.

To prepare the data, we used SPSS version 25.0 (IBM, 2021 ) and Mplus version 8.4 (Muthén & Muthén, 1998–2019 ). First, we calculated scale scores when at least 70% of the items for that scale were answered. After this step, only cases without missing values on the main variables necessary to calculate TJA (i.e., ‘cognitive ability’ and ‘teacher judgment of student cognitive ability’) were retained within the sample. Furthermore, we deleted cases with missing values on the covariates for PSM.

2.4.2 Reliability estimation

We estimated reliability for our scales measuring the socio-emotional experiences of school and for the scale measuring the teacher judgments of cognitive ability. As a first indicator, we estimated Cronbach’s alpha ( α ). Cronbach’s alpha has some prerequisites such as tau equivalence, uncorrelated residuals, and normally distributed items (McNeish, 2018 ). Therefore, we also estimated the greatest lower bound (GLB; McNeish, 2018 ; Trizano-Hermosilla & Alvarado, 2016 ) and Raykov’s omega ( ω ; Raykov, 1997 ) as reliability indicators with less stringent assumptions. We used confirmatory factor analysis (CFA) to estimate Raykov’s omega and to test whether essential tau equivalence holds. Further model information and the results of the CFA are included in Appendix B (supplementary materials).

2.4.3 Underestimated, accurately judged, and overestimated students

In previous studies, different cut-offs were chosen for accurate vs. inaccurate teacher judgments. Some researchers argued that any value different from the actual cognitive ability constitutes inaccurate judgments (e.g., Urhahne et al., 2011 ; Zhou & Urhahne, 2013 ). Others specified specific cut-off values, for example, inaccuracies of at least 0.25 SD (e.g., De Boer et al., 2010 ) or 0.5 SD (e.g., Urhahne, 2015 ). We set the cut-off at 0.25 SD , as, on the one hand, teachers cannot be expected to always judge their students perfectly (Urhahne, 2015 ); on the other hand, slight inaccuracies need to be treated as such (De Boer et al., 2010 ). Hence, our three TJA groups were z < -0.25 (overestimated students), z  = − 0.25 to 0.25 (accurately judged students), and z  > 0.25 (underestimated students).

2.4.4 Linear discriminant analyses

We used the matched data to calculate linear discriminant analyses in SPSS version 25.0 (IBM, 2021 ). The method has the advantage of examining students’ SEES in the form of a multidimensional profile of student experiences rather than comparing students on individual dimensions. First, the results show the extent to which the linear combination of all SEES dimensions discriminates between n  = 3 TJA groups (overestimated students, accurately judged students, underestimated students)—in other words, the extent to which the groups differ in their experiences related to the learning and social environment ( Hypothesis 1 ). Secondly, the results show the relative importance of each dimension within the linear combination for the discriminant function—in other words, the contribution of each dimension to the differentiation of underestimated vs. accurately judged vs. overestimated students ( Hypothesis 2 ). Altogether, n -1 discriminant functions are calculated in each analysis (i.e., 2 functions). The second function explains the variance that is not explained by the first function and therefore does not necessarily have to be significant to interpret the results (see Rudolf & Buse, 2020 ).

We carried out the discriminant analysis for each PSM sample. The quality of the linear discriminant analyses was tested by the following parameters (Backhaus et al., 2015 ): The Eigenvalue 𝛾 indicates the relation of explained to unexplained variance. The canonical correlation c indicates how much of the total variance of the discriminant values can be explained by the discriminant function. Both values (𝛾 and c ) are to be as large as possible. Wilks-Lambda ( Λ ) describes the relation of unexplained variance to total variance; subsequently, small values correspond to high discriminant qualities of the discriminant function. The inferential statistical χ 2 test demonstrates whether the linear combination significantly determines the group differences between overestimated versus accurately judged versus underestimated students. Finally, the overall classification rate reveals the assignment accuracy to the three TJA groups by the linear combination of students’ SEES.

3.1 Descriptive statistics

Missing values for the dimensions of SEES were ≤ 2.50%, with the exception of joy of learning (4.12%) and feeling of being accepted (4.19%). Descriptive statistics of the continuous variables can be found in Table 4 . There were no meaningful differences from the overall norm sample (see last column in Table 4 ). Data were normally distributed, except for willingness to make an effort, which was left-skewed. Reliability was acceptable to excellent for most of the scales (except for two scales: willingness to make an effort and class climat

Bivariate correlations with corrected standard errors for all analysis variables for the full study sample are reported in Table  5 . All dimensions of the SEES correlated positively with each other ( r  = .18 to .71, p  < .01). Teacher judgments of students’ cognitive ability positively correlated with almost all variables. TJA correlated weakly or not significantly with the dimensions of the SEES (all correlations < .10). Moreover, TJA strongly correlated with students’ cognitive ability ( r  = .82, p  < .01) and slightly correlated with parental education ( r  = .17, p  < .01). This means that low cognitive ability and lower parental education was more likely associated with overestimation, whereas high cognitive ability and higher parental education was more likely associated with underestimation.

3.2 Propensity score matching

3.2.1 balance estimation.

Relative influences of matching covariates on the PSs were 95.86% for students’ cognitive ability, 2.89% for parental education, 0.94% for teacher acquaintance with the student, 0.23% for students’ gender, and 0.07% for students’ native language. Table 6 shows balance criteria for the different matching solutions. For both the unrestricted (Sample 1) and the exact matching approach (Sample 2), the best d /max d model was preferable. This model reduced (almost) all pairwise effects below the threshold of a small effect size ( d  < 0.20), showed comparable balance on other estimates, but excelled in having a considerably larger sample size per group compared to the best mean g or the best Pillai’s trace solutions. Thus, the selected analytic sample consisted of n  = 348 for matching without restrictions and n  = 312 for exact matching. Further statistics regarding the sample and the balance are reported in Appendix A .

3.2.2 Subsample comparisons

Table 7 shows the central tendency and dispersion for all matching covariates, including subsample comparisons using appropriate standardized difference values according to the respective scale of measurement (Sedlmeier & Renkewitz, 2013 ). In the full study sample, students in the three TJA groups differed in their cognitive ability, with large effect sizes ( d = -1.34 to -2.47). Underestimated students on average had the highest cognitive ability ( M  = 114.86, SD  = 10.69), accurately judged students were in the middle ( M  = 102.17, SD  = 8.58), and overestimated students had the lowest cognitive ability ( M  = 89.54, SD  = 9.77). Moreover, students differed in their native language and their parental education, with small effect sizes ( w  = 0.24 to 0.25, d = -0.38). Overestimated students showed a lower proportion of German native speakers compared to other languages (73.9%) than accurately judged (81.1%) or underestimated students (80.7%). Furthermore, overestimated students had a lower parental education ( M  = 3.66, SD  = 1.14) compared to underestimated students ( M  = 4.11, SD  = 1.21).

Overall, appropriate balance was achieved by PSM. In matched Sample 1, all differences in students’ cognitive ability were smaller than 0.10. We found some small differences in students’ native language (w  = 0.29): accurately judged students were more often German native speakers compared to underestimated students. In matched Sample 2, two pair comparisons exceeded the target value for Cohen’s d : underestimated students had a higher cognitive ability than accurately judged ( d = -0.55) and overestimated ( d = -0.73) students.

3.3 Linear discriminant analyses

3.3.1 assumptions.

We tested the assumptions of linear discriminant analyses by bivariate correlations ( independence ), boxplots ( outliers ), and histograms ( normal distribution ) of the dimensions of SEES (Büyüköztürk & Çokluk-Bökeoğlu, 2008 ). There was no multicollinearity in either matched sample ( r  ≤.65 Sample 1 , r  ≤.71 Sample 2 ). Furthermore, in both samples, three dimensions had no outliers (academic self-concept, attitude towards school, class climate), and the other dimensions had one to ten outliers. Finally, all distributions visually appeared skewed to the left. However, we only identified critical skewness and kurtosis values for willingness to make an effort both in Sample 1 ( skewness = -2.15, SE  = 0.13; kurtosis  = 5.52, SE  = 0.26) and Sample 2 ( skewness = -2.15, SE  = 0.14; kurtosis  = 4.78, SE  = 0.28).

3.3.2 Main analyses

Due to their low reliability, we conducted the linear discriminant analyses with and without (see Appendix C, supplementary materials) the variables willingness to make an effort and class climate. There were no meaningful statistical differences in the findings: in both cases, the χ 2 test was significant for discriminant function 1 but not for discriminant function 2. Further, the correlations within the structure matrix were very similar (see Table  10 and Table C.2). Finally, the quality parameters of both analyses (see Table  8 and Table C.1) were very similar (i.e., 𝛾 = Eigenvalue, which indicates the ratio of explained to unexplained variance; c =  canonical correlation, which indicates how much variance can be explained by the discriminant function; Λ  = Wilks-Lambda, which describes the ratio of unexplained to total variance; % correct classification, which indicates the assignment accuracy to the groups). In the following, we therefore report the results of the analyses calculated with all variables.

Table 8 shows that the linear combination of all dimensions of SEES significantly determined differences between the three TJA groups for both matched samples ( Hypothesis 1 ). Only discriminant function 1 was significant. Quality parameters were acceptable and very similar in both matching samples, with a slightly higher correct classification rate in Sample 1 compared to Sample 2 (45.0 vs. 42.0%). Within the three TJA groups, overestimated and underestimated students had the highest classification rates (Sample 1: 61.5 and 56.3%; Sample 2: 51.5 and 46.5%).

Discriminant functions at group centroids and discriminant values for the three TJA groups. Note OE = overestimation, AJ = accurate judgment, UE = underestimation

In order to take a closer look at differences between the three TJA groups, we calculated discriminant function 1 at group centroids (i.e., the mean of the discriminant function scores by group; see Table 9 ; Fig.  1 ). Greater differences in values represent greater differences between the groups. Discriminant values and the group centroid of discriminant function 1 were on the negative side for underestimated students, in the middle for accurately judged students, and on the positive side for overestimated students. In both matched samples, differences between accurately judged students and overestimated students were similar to those for accurately judged students and underestimated students. In contrast, there were substantially larger differences between overestimated and underestimated students. To examine the direction of differences, we calculated the structure matrix (see Table 10 ). It shows the correlation of each dimension with discriminant function 1. All dimensions showed positive correlations, implying that more positive scores on the dimensions of SEES were associated with more positive scores on discriminant function 1—this means that positive scores were closest to overestimation, less close to accurate judgments, and least close to underestimation (see Fig.  1 ). Given the significant difference between the three TJA groups (see Table 8 ), the SEES of school were more positive for overestimated compared to accurately judged students and underestimated students ( Hypothesis 1a ) and more positive for accurately judged students compared to underestimated students ( Hypothesis 1b ).

The next step was considering the relative importance of the dimensions of SEES for the discriminant function (i.e., dimensions on which students differed; Hypothesis 2 ). High correlations within the structure matrix (see Table 10 ) indicate a high relative importance of the dimension for the classification. In line with the effect size classification used to present the studies in Table  3 , we considered medium and large correlations ( r  ≥.3 or close to 0.3) as important for the classification (Cohen, 1988 ). Students’ academic self-concept was most important in determining differences between the three groups of TJA (both samples; r  = .965 or .740). Willingness to make an effort also considerably contributed to the group discrimination (both samples; r  = .406 or .577). Furthermore, in both samples, joy of learning and feeling of being accepted made an important contribution, however, with some slight differences between the samples. In Sample 1, the joy of learning had a higher relative importance than the feeling of being accepted ( r  = .407 vs .277); in Sample 2, the reverse pattern was present ( r  = .297 vs .413). The other variables (i.e., attitude towards school, social integration, and class climate) did not contribute meaningfully.

Figure  2 shows the characteristic profiles (based on z -standardized group means) for students in the three TJA groups (compact bars = experience of the learning environment, striped bars = experience of the social environment). In both samples, overestimated students experienced school more positively than the average of all students, whereas accurately judged students lay in the middle, and underestimated students experienced school more negatively than the average of all students. This pattern was also evident for the individual dimensions that emerged as particularly important within the linear discriminant analyses (academic self-concept and willingness to make an effort). For the joy of learning, differences descriptively existed between overestimated and accurately judged or underestimated students but less between accurately judged and underestimated students. Overestimated students had an above-average enjoyment of learning, whereas accurately judged and underestimated students lay near the average. For the feeling of being accepted, differences descriptively existed between underestimated and accurately judged or overestimated students but less between overestimated and accurately judged students. Underestimated students felt less accepted by their teachers than the average, whereas accurately judged and overestimated students lay near the average. Altogether, the results show that differences between overestimated, accurately judged, and underestimated students are stronger for dimensions of the experience of the learning environment (differences for the academic self-concept, willingness to make an effort, and joy of learning) than for dimensions of the experience of the social environment (differences found only for the feeling of being accepted; Hypothesis 2 ).

Characteristic profiles (based on z -standardized group Means) for students of the three TJA groups. Note OE = overestimation, AJ = accurate judgment, UE = underestimation

4 Discussion

4.1 summary.

In the present study, we investigated whether students differed in their SEES depending on the TJA of their cognitive ability. Furthermore, within the characteristic profile of various dimensions of SEES, we investigated whether possible differences were stronger for the experience of the learning environment compared to the experience of the social environment. We controlled possibly confounding variables by using PSM, which enables building groups of students who are comparable regarding these variables. Groups of overestimated, accurately judged, and underestimated students were then compared by linear discriminant analyses.

In line with Hypothesis 1, we found that students who were overestimated in their cognitive ability had more positive SEES than students who were accurately judged or underestimated ( Hypothesis 1a ), and students who were accurately judged had more positive SEES compared to students who were underestimated ( Hypothesis 1b ). In line with Hypothesis 2, we found that the three TJA groups especially differed in their academic self-concept, their willingness to make an effort, and their joy of learning (which are dimensions of the experience of the learning environment), but also, and not as expected, in their feeling of being accepted by teachers (which relates to the experience of the social environment). We found no substantial differences in students’ attitude towards school, social integration, and perceived class climate.

4.2 Matching samples

We compared overestimated, accurately judged, and underestimated students who were comparable in their cognitive ability, gender, parental background, and their teachers’ acquaintance with them. We used PSM to match students on these potentially confounding factors. In research on over- and underestimated students, PSM has not been used before. In general, few studies have (systematically) controlled for confounding factors (e.g., Gniewosz & Watt, 2017 ; Urhahne, 2015 ; see Table  3 ). Compared to regression analysis or analysis of variance, PSM has the particular strength that information on common variance of covariates is preserved.

Two samples were created, with one that ignored the nested structure of the data (Sample 1) and one that additionally matched students based on their classroom (Sample 2). Both of the resulting matching samples had strengths and limitations. There were no relevant differences between students of the three TJA groups in almost all matching covariates in Sample 1. However, the process did not control for the possible influence of the class context or the judging teacher. By contrast, Sample 2 considered this limitation, but students of the three TJA groups still differed in their cognitive ability with underestimated students having higher cognitive abilities than accurately judged or overestimated students.

4.3 How are students’ socio-emotional experiences of school related to teachers’ judgment accuracy?

There were no differences in findings between the matching samples with regard to Hypothesis 1. That is, findings were robust, with and without controlling for the nested data structure. Our findings with respect to Hypothesis 1 are well aligned with previous studies on SEES depending on TJA. Most studies compared students who were overestimated and underestimated in their achievement. The studies robustly suggest that overestimated compared to underestimated students are at advantage with respect to their SEES (e.g., Rubie-Davies & Peterson, 2016 ; Urhahne, 2015 ; Urhahne et al., 2010 ). Our findings are also well aligned with findings showing that overestimation is not only more beneficial than underestimation, but also more beneficial than accurate judgments— and that accurate judgments are more beneficial than underestimation for students’ SEES (Baudson, 2011 ; Gentrup et al., 2020 ). The findings are also consistent with studies showing a relation between students’ achievement and TJA, which demonstrated that overestimated compared to underestimated students have a higher academic achievement (Baudson, 2011 ; Gentrup et al., 2020 ; Rubie-Davies et al., 2014 ; Rubie-Davies & Peterson, 2016 ; Urhahne, 2015 ) and develop more positively in their achievement (De Boer et al., 2010 ; Stang & Urhahne, 2016 ). Furthermore, in a longitudinal study by Rubie-Davies et al. ( 2020 ), TJA at class level predicted students’ academic achievement and their perceived teacher support (higher values for students in overestimated compared to underestimated classrooms).

One plausible explanation for these findings is the self-fulfilling prophecy —a false definition of the situation evoking a behavior which makes the original false expectation come true (Merton, 1948 ). This explanatory approach relates to the so-called expectation effects, which have often been studied in connection with achievement outcomes. In their systematic review, Wang et al. ( 2018 ) found that in primary and secondary schools, positive teacher expectations for students’ achievement were mostly associated with positive concurrent and future student outcomes. This included achievement outcomes, psychosocial outcomes such as students’ self-concept and academic motivation, and other school-related behavioral outcomes. The findings on the relation between teacher expectations and psychosocial outcomes are also in line with the above described 6-step teacher-student interaction model by Brophy and Good ( 1970 ) and Brophy ( 1983 ).

Teacher expectation effects imply reciprocal effects between TJA and students’ SEES over time. Findings of two longitudinal studies found that initial TJA influenced change in students’ SEES (Baudson, 2011 ; Gniewosz & Watt, 2017 ). At the same time, a teacher’s perception of a student’s SEES might affect the teacher’s judgment of that student’s abilities. In line with this assumption, Gnas et al. ( 2022a ) found that teachers judged comparable students’ cognitive abilities higher for those students who had higher academic self-concepts. Similarly, in their review, Wang et al. ( 2018 ) found that socio-psychological factors such as self-concept, self-efficacy, and self-expectations partially mediated teacher expectation effects on academic achievement.

Overall, our results imply that, similar to TJA of achievement (e.g., Urhahne, 2015 ;Urhahne et al., 2010 , 2011 ), students differ depending on whether they are overestimated, accurately judged, or underestimated in their cognitive ability by their teachers. Our findings support the assumption that overestimation is more beneficial than accurate judgments and that underestimation has the most negative effects.

4.4 Experience of the learning versus social environment

The results of the present study revealed that students who were overestimated, accurately judged, and underestimated in their cognitive ability, especially differed in their academic self-concept, willingness to make an effort, joy of learning, and feeling of being accepted by their teachers. That is, and in accordance with Hypothesis 2 , TJA was primarily related to the experience of the learning environment and less related to the experience of the social environment. Students differed most in their academic self-concept compared to other dimensions of the learning environment (for similar findings, see Baudson, 2011 ; Urhahne, 2015 ; Urhahne et al., 2010 ; Urhahne et al., 2011 ). These findings might be explained by the fact that the teacher judgments referred to an achievement- and learning-related construct (i.e., cognitive ability) and not to social variables such as social competence or popularity. Therefore, possible effects of TJA should be seen more strongly in students’ achievement- and learning-related experiences.

However, and not as expected, overestimated or accurately judged and underestimated students also differed in their feeling of being accepted by their teacher among students in Sample 2, but not in Sample 1. In Sample 2, students who were underestimated felt less accepted by their teachers than the other groups. These students also had higher cognitive abilities than the other groups. How can one explain the different findings between the two matching samples for the feeling of being accepted by the teacher? In Sample 1, differences between teachers in their individual propensity to give their students the feeling of being accepted—independently of over- or underestimating individual students’ cognitive ability—might have masked differences between the three TJA student groups. When controlling for this between-teacher variability, more cognitively able students who were underestimated by their teachers felt less accepted. This finding might indicate that for more cognitively able students, the acknowledgement of their cognitive ability is especially important for their feeling of acceptance by others. However, this interpretation has to be treated with caution as it is only done a posteriori and the finding could alternatively be explained by statistical differences (e.g., larger variability of cognitive abilities in Sample 2). A more complex explanation could be derived from Self-Determination Theory (Deci & Ryan, 1985 ), according to which the needs for competence, autonomy, and relatedness determine individuals’ motivation and actions. Students who feel accepted by their teachers are more likely to engage in actions that they believe will be evaluated positively by the teacher. For example, they exert themselves, participate in lessons, and work conscientiously. This might tempt teachers to overestimate these students’ abilities. In contrast, students who feel less accepted by their teacher might engage less in actions that are positively evaluated by their teachers. This might tempt teachers to underestimate these students. However, these assumptions are rather speculative and need further testing with longitudinal data.

4.5 Practical implications

Our findings clearly demonstrated that primary school students who were underestimated in their cognitive ability were at a disadvantage in terms of their SEES compared to primary school students who were accurately judged and those who were overestimated. Although no causal effects of TJA on students’ SEES can be tested by the present cross-sectional findings, the link between TJA and SEES implies that there is a need to pay closer attention to underestimated students and to support and encourage them in their SEES if necessary.

To become aware of misjudgments in the first place, an important implication for teachers is to reflect on one’s own judgment accuracy. A procedure based on a 5-step diagnostic cycle by Wahl et al. ( 2007 ) is one option for this. (1) In a first step, teachers choose a student characteristic they want to judge (e.g., achievement in mathematics). (2) Next, they predict this characteristic for their students (e.g., predicting their grade in the next mathematics test). (3) In a third step, they measure the chosen characteristic (e.g., grading the mathematics test). (4) Next, teachers compare their prediction with the result of the measurement. (5) In a final step, teachers reflect on the result, particularly with respect to discrepancies. In the event of misjudgments, teachers should ask themselves about reasons for these misjudgments. Are misjudgments (especially underestimation), for instance, due to student characteristics (e.g., their gender or native language)? Or, which assessment standards did I use (e.g., social vs. criterial reference norm)? A repeated use of this 5-step diagnostic cycle can also help to indicate improvements in TJA. The use of this tool should be accompanied by information on typical judgment errors, teacher expectation effects, and basic information on educational assessment.

In the present study, we used a primary school sample. This focus is relevant, as the primary school years are an important starting point for future academic and socio-emotional development (Aviles et al., 2006 ). Overall, the pattern of findings seems to be the same in primary and secondary school (see Table  3 ). Previous studies, however, mostly examined only Grade 4 in primary school (Urhahne et al., 2010 , 2011 ; Zhou & Urhahne, 2013 ). Our findings suggest that TJA might play an important role for students’ SEES not only at this point in time but throughout the entire primary school years. Therefore, teachers should reflect on their own TJA as early as Grade 1.

Another important implication of the present study is that students should be supported in their SEES. The findings suggest that in particular underestimated students may need this support. The present findings specifically imply interventions for fostering students’ academic self-concept. Students’ experience of school and feedback with regard to their performance or achievement—especially from important reference persons such as teachers—influence the development of their academic self-concept (Moschner & Dickhäuser, 2018 ). Teachers can support their students’ academic self-concept by using an individual reference norm. For example, they can provide feedback on individual developmental progress, and they can adapt requirements to individual student competencies (e.g., providing tasks of varying difficulty). Furthermore, they can provide positive performance feedback related to effort rather than abilities, thus creating a sense of achievement. At the class level, teachers can support their students’ academic self-concept by establishing a positive classroom climate (e.g., through clear rules, a calm working atmosphere, and an appreciative culture of dealing with mistakes; Langenkamp, 2018 ; Trautwein & Möller, 2016 ). Prior research shows that social support and positive teacher-student interactions (Aviles et al., 2006 ; Hofmann & Siebertz-Reckzeh, 2008 ) contribute to more positive perceptions of the classroom climate as well as the development of socio-emotional competencies. A positive teacher-student relationship starts with the teacher and manifests in behaviors such as closeness, warmth, care, and support (Bouchard & Smith, 2017 ; Inman, 2019 ), as well as quantitative indicators, such as the amount of interaction with a specific student, and qualitative indicators, such as type and tone of feedback a teacher provides (see Endedijk et al., 2022 ).

It can be summarized that the present findings indicate on the one hand a need for action in terms of teachers’ reflection of their judgment accuracy and its improvement in the event of systematic underestimation. On the other hand, a practical implication for teachers is the importance of supporting students in their SEES, paying special attention to underestimated students.

4.6 Limitations

Although the present study has clear strengths, such as the large sample covering students in Grades 1 to 4, the assessment of multiple dimensions of students’ SEES, and the use of PSM that allowed us to compare the three TJA groups within a quasi-experimental design, our study also has limitations. One limitation concerns the cross-sectional analysis of the data. Although some of the findings are discussed causally, an actual causal interpretation of the findings is not possible. Another limitation concerns the fact that students’ school grade could not be added as a matching covariate, as it was not available in Grades 1 and 2. Furthermore, there was limited variance in students’ cognitive ability in the matched samples, because of the difficulty in finding matches for students from the more extreme cognitive ability groups who are also similar in all other matching covariates. In addition, two out of three Cohen’s d’ s (for differences in students’ cognitive ability) in our creation of the three TJA groups in Sample 2 exceeded the desired value of 0.2, as it is unlikely to find overestimated, accurately judged, and underestimated students with similar cognitive abilities within one classroom. Nevertheless, PSM was able to considerably reduce the large Cohen’s d’ s representing differences between overestimated, accurately judged and underestimated students of the full Sample ( d full sample = -1.34 to -2.47 vs. d Sample 2 = -0.19 to -0.73). Furthermore, the strength of Sample 2 lies in accounting for the nested data structure. A further limitation relates to the rather moderate classification rate within linear discriminant analyses, which would probably have been larger if only overestimated and underestimated students were compared.

Moreover, two scales had low reliabilities (i.e., willingness to make an effort and class climate). Such low reliabilities are a known issue of short scales (Rammstedt & Beierlein, 2014 ; Ziegler et al., 2014 ), which often include comparatively heterogeneous items to retain content validity (Loevinger, 1954 ). However, reliabilities of the other short scales used in this study were acceptable to excellent. For the willingness to make an effort scale, its deviation from normality may result in Cronbach’s alpha underestimating the reliability (Sheng & Sheng, 2012 ; Xiao & Hau, 2023 ). For the class climate scale, its lack of essential tau-equivalence (see Table B.1; supplementary materials) may explain its low Cronbach’s alpha. When we estimated reliability with indicators less prone to deviations from normality and that do not assume tau-equivalence (i.e., GLB and Raykov’s omega), reliabilities of both scales only slightly increased to .60. However, excluding the scales class climate and willingness to make an effort from the analyses did not change the pattern of results. Furthermore, lower internal consistencies are acceptable if group differences—and not individual differences—are investigated (Nunnally & Bernstein, 1994 ).

Two final limitations concern the secondary analyses of the data as well as the representativity of the present sample in terms of students’ educational background and native language. Children with a migration background and children whose parents had lower school leaving certificates were underrepresented in the present sample (migration background: 22.1% in the present sample vs. 40.4% of all German primary school students; Federal Statistical Office, 2019 ; higher education degree: 35.8% in the present sample vs. 17.6% in the population, Federal Statistical Office, 2020 ).

4.7 Outlook and conclusion

Given the cross-sectional data, we described students’ characteristic profiles with regard to their SEES depending on whether they were overestimated, accurately judged, or underestimated by their teachers. It would be interesting to investigate the presumed direction of the effect of TJA on SEES in a longitudinal study. Additionally, it would be interesting to investigate the reciprocal effects of TJA and SEES. It is possible that overestimation and underestimation in the long term manifest themselves in a positive and negative spiral, respectively.

Overall, the present results suggest that primary school students who are overestimated in their cognitive ability by their teachers socio-emotionally experience school most positively, followed by students who are accurately judged. Students who are underestimated in their cognitive ability experience school most negatively and, most importantly, below the average of all students. We conclude that teachers’ attention in terms of the support of SEES might be directed especially to underestimated students. This particularly applies with regard to the experience of the learning environment and the feeling of being accepted by the teacher. Furthermore, the present results imply that teachers should assess and reflect on their TJA of cognitive ability. Encouragingly, inaccuracy only seems to be negative when directed toward underestimation. This suggests that teachers and researchers should focus primarily on reasons for underestimating students.

Academic achievement can be defined as “performance outcomes that indicate the extent to which a person has accomplished specific goals that were the focus of activities in instructional environments, specifically in school, college, and university.” […] (Steinmayr et al., 2014 ). It is mostly operationalized by school grades or standardized test performance. Cognitive ability can be defined as “any ability that concerns some kind of cognitive task” as “any task in which correct and appropriate processing of mental information is critical to successful performance” (Carroll, 1993 , p.10). The relation between cognitive ability and academic achievement is high but far from perfect. Meta-analyses reveal 29% common variance for school grades (Roth et al., 2015 ) and 54% common variance for standardized achievement tests (Zaboski et al., 2018 ).

The studies used the following effect parameters: d (Urhahne et al., 2011 ), β (Baudson, 2011 ; Gniewosz & Watt, 2017 ), r (Rubie-Davies & Peterson, 2016 ), and η 2 p (Stang & Urhahne, 2016 ; Urhahne, 2015; Zhou & Urhahne, 2013 ). The categorization into different effect sizes was based on Cohen’s ( 1988 ) classification, according to which d  ≥ 0.2 is regarded as a small effect, d  ≥ 0.5 as a medium effect, and d  ≥ 0.8 as a large effect. β , r , and η 2 p were converted correspondingly (Lenhard & Lenhard, 2016 ).

Alvidrez, J., & Weinstein, R. S. (1999). Early teacher perceptions and later student academic achievement. Journal of Educational Psychology , 91 (4), 731–746. https://doi.org/10.1037/0022-0663.91.4.731 .

Article   Google Scholar  

Aviles, A. M., Anderson, T. R., & Davila, E. R. (2006). Child and adolescent social-emotional development within the context of school. Child and Adolescent Mental Health , 11 (1), 32–39. https://doi.org/10.1111/j.1475-3588.2005.00365.x .

Backhaus, K., Erichson, B., Weiber, R., & Plinke, W. (2015). Diskriminanzanalyse [Discriminant analysis]. In K. Backhaus, B. Erichson, W. Plinke, & R. Weiber (Eds.), Multivariate Analysemethoden: Eine Anwendungsorientierte Einführung [Multivariate analysis methods: An application-oriented introduction] (14th ed., pp. 215–282). Springer. https://doi.org/10.1007/978-3-662-46076-4_5 .

Baudson, T. G. (2011). The (mis-)measure of children’s cognitive abilities [Dissertation]. University of Trier, Trier.

Baudson, T. G., & Preckel, F. (2015). Der FEESS-K. Eine Kurzversion des FEESS von Rauer und Schuck zum Einsatz in Klassenstufe 1 bis 4 [A short version of FEESS for grades 1 to 4 by Rauer and Schuck] [Unpublished instrument]. University of Trier.

Baudson, T. G., Fischbach, A., & Preckel, F. (2016a). Teacher judgments as measures of children’s cognitive ability: A multilevel analysis. Learning and Individual Differences , 52 , 148–156. https://doi.org/10.1016/j.lindif.2014.06.001 .

Baudson, T. G., Wollschläger, R., & Preckel, F. (2016b). THINK 1–4: Test zur Erfassung der Intelligenz im Grundschulalter [THINK 1–4: Test for assessing intelligence at primary school age (1st edition)] (1st ed.). Hogrefe Schultests.

Baumert, J., & Kunter, M. (2011). Das Kompetenzmodell von COACTIV [The COACTIV competence model]. In M. Kunter, J. Baumert, W. Blum, U. Klusmann, Krauss, s., & M. Neubrand (Eds.), Professionelle Kompetenz von Lehrkräften: Ergebnisse des Forschungsprogramms COACTIV [Professional competence of teachers: results of the research program COACTIV] (pp. 29–56). Waxmann.

Bergold, S., Kasper, D., Wendt, H., & Steinmayr, R. (2020). Being bullied at school: The case of high-achieving boys. Social Psychology of Education , 23 (2), 315–338. https://doi.org/10.1007/s11218-019-09539-w .

Bergold, S., Weidinger, A. F., & Steinmayr, R. (2021). The big fish from the teacher’s perspective: A closer look at reference group effects on teacher judgments. Journal of Educational Psychology , 114 (3), 656–680. https://doi.org/10.1037/edu0000559

Bouchard, K. L., & Smith, J. D. (2017). Teacher-student relationship quality and children’s bullying experiences with peers: Reflecting on the mesosystem. The Educational Forum , 81 (1), 108–125. https://doi.org/10.1080/00131725.2016.1243182 .

Brophy, J. E. (1983). Research on the self-fulfilling prophecy and teacher expectations. Journal of Educational Psychology , 75 (5), 631–661. https://doi.org/10.1037/0022-0663.75.5.631 .

Brophy, J. E., & Good, T. L. (1970). Teachers’ communication of differential expectations for children’s classroom performance: Some behavioral data. Journal of Educational Psychology , 61 (5), 365–374. https://doi.org/10.1037/h0029908 .

Brunner, M., Keller, U., Dierendonck, C., Reichert, M., Ugen, S., Fischbach, A., & Martin, R. (2010). The structure of academic self-concepts revisited: The nested Marsh/Shavelson model. Journal of Educational Psychology , 102 (4), 964–981. https://doi.org/10.1037/a0019644 .

Büyüköztürk, Ş., & Çokluk-Bökeoğlu, Ö. (2008). Discriminant function analysis: Concept and application. Eurasian Journal of Educational Research , 33 , 73–92.

Google Scholar  

Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies . Cambridge University Press.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.

De Boer, H., Bosker, R. J., & van der Werf, M. P. C. (2010). Sustainability of teacher expectation bias effects on long-term student performance. Journal of Educational Psychology , 102 (1), 168–179. https://doi.org/10.1037/a0017289 .

De Fruyt, F., Wille, B., & John, O. P. (2015). Employability in the 21st century: Complex (interactive) problem solving and other essential skills. Industrial and Organizational Psychology , 8 (2), 276–281. https://doi.org/10.1017/iop.2015.33 .

Deci, E., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior . Spinger.

Eder, F. (2018). Schul- und Klassenklima [School and class climate]. In D. H. Rost, J. R. Sparfeldt, & S. Buch (Eds.), Handwörterbuch pädagogische Psychologie [Handbook of educational psychology] (5th ed., pp. 696–707). Beltz.

Emslander, V., Holzberger, D., Ofstad, S., Fischbach, A., & Scherer, R. (2023). Teacher-Student Relationships and Student Outcomes: A Systematic Review of Meta-Analyses and Second-Order Meta-Analysis . https://doi.org/10.31234/osf.io/qxntb .

Endedijk, H. M., Breeman, L. D., van Lissa, C. J., Hendrickx, M. M. H. G., den Boer, L., & Mainhard, T. (2022). The teacher’s invisible hand: A meta-analysis of the relevance of teacher-student relationship quality for peer relationships and the contribution of student behavior. Review of Educational Research , 92 (3), 370–412. https://doi.org/10.3102/00346543211051428 .

Federal Statistical Office (2019). Population and employment: Population with a Migration Background– Results of the 2018 Microcensus . https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Migration-Integration/Publikationen/Downloads-Migration/migrationshintergrund-2010220187004.pdf?__blob=publicationFile .

Federal Statistical Office (2020). Educational level . https://www.destatis.de/EN/Themes/Society-Environment/Education-Research-Culture/Educational-Level/_node.html .

Fisher, Z., Tipton, E., & Zhipeng, H. (2017). Robust variance meta-regression [R package robumeta version 2.0] . Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/robumeta/index.html .

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research , 74 (1), 59–109. https://doi.org/10.3102/00346543074001059 .

Gentrup, S., Lorenz, G., Kristen, C., & Kogan, I. (2020). Self-fulfilling prophecies in the classroom: Teacher expectations, teacher feedback and student achievement. Learning and Instruction , 66 , 101296. https://doi.org/10.1016/j.learninstruc.2019.101296 .

Gnas, J., Mack, E., Matthes, J., Breit, M., & Preckel, F. (2022a). Sozio-emotionales Erleben von Schule bei Grundschülerinnen und -schülern: Zusammenhänge mit intellektueller Begabung und Schulleistung [Primary school students’ socio-emotional experiences of school: Relations with students’ intellectual ability and school achievement]. Zeitschrift für Erziehungswissenschaft , 25 , 1095–1123. https://doi.org/10.1007/s11618-022-01115-x

Gnas, J., Mack, E., & Preckel, F. (2022b). When classmates influence teacher judgment accuracy of students’ cognitive ability: Studying frame-of-reference effects in primary school. Contemporary Educational Psychology , 69, , 102070. https://doi.org/10.1016/j.cedpsych.2022.102070

Gniewosz, B., & Watt, H. M. G. (2017). Adolescent-perceived parent and teacher overestimation of mathematics ability: Developmental implications for students’ mathematics task values. Developmental Psychology , 53 (7), 1371–1383. https://doi.org/10.1037/dev0000332 .

Golle, J., Schils, T., Borghans, L., & Rose, N. (2023). Who is considered gifted from a teacher’s perspective? A representative large-scale study. Gifted Child Quarterly , 67 (1), 64–79. https://doi.org/10.1177/00169862221104026 .

Götz, T., Frenzel, A. C., & Pekrun, R. (2008). Sozialklima in der Schule [Social climate in school]. In W. Schneider, M. Hasselhorn, & J. Bengel (Eds.), Handbuch der Psychologie: Vol. 10. Handbuch der pädagogischen Psychologie [Handbook of edcuational psychology] (pp. 503–514). Hogrefe. http://kops.uni-konstanz.de/handle/123456789/1662 .

Grewe, N. (2017). Soziale Interaktion und Klassenklima [Social interaction and class climate]. In M. K. Schweer (Ed.), Lehrer-Schüler-Interaktion. Inhaltsfelder, Forschungsperspektiven und methodische Zugänge [Teacher-child interaction. Content areas, research perspectives and methodological approaches] (3rd ed., pp. 547–560). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-15083-9_24 .

Gu, X. S., & Rosenbaum, P. R. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. Journal of Computational and Graphical Statistics , 2 (4), 405–420. https://doi.org/10.1080/10618600.1993.10474623 .

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement . Routledge.

Heller, S. S., Rice, J., Boothe, A., Sidell, M., Vaughn, K., Keyes, A., & Nagle, G. (2012). Social-emotional development, school readiness, teacher-child interactions, and classroom environment. Early Education & Development , 23 (6), 919–944. https://doi.org/10.1080/10409289.2011.626387 .

Hofmann, H., & Siebertz-Reckzeh, K. (2008). Sozialisationsinstanz Schule: Zwischen Erziehungsauftrag und Wissensvermittlung [Socialization instance school: Between educational mission and knowledge transfer]. In M. K. W. Schweer (Ed.), Lehrer-Schüler-Interaktion. Inhaltsfelder, Forschungsperspektiven und methodische Zugänge [Teacher-child interaction. Content areas, research perspectives and methodological approaches] (2nd ed., pp. 13–38). VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-531-91104-5_1

Hoge, R. D., & Coladarci, T. (1989). Teacher-based judgments of academic achievement: A review of literature. Review of Educational Research , 59 (3), 297–313. https://doi.org/10.3102/00346543059003297 .

IBM. (2021). SPSS statistics for Windows, Version 28.0 . IBM Corp.

Inman, C. (2019). Examining teacher-student relationships: Moving from bullying to caring [Dissertation]. Southern Illinois University Edwardsville, Edwardsville, IL.

Johns, S. (2020). Social competence growth in preschool: Individual differences and the role of classroom context [Dissertation]. Arizona State University, Tempe, AZ.

Jung, W., Thompson, H. J., & Byun, E. (2022). Social integration: A concept analysis. Nursing Forum , 57 (6), 1551–1558. https://doi.org/10.1111/nuf.12843 .

Korpershoek, H., Harms, T., De Boer, H., van Kuijk, M., & Doolaard, S. (2016). A meta-analysis of the effects of classroom management strategies and classroom management programs on students’ academic, behavioral, emotional, and motivational outcomes. Review of Educational Research , 86 (3), 643–680. https://doi.org/10.3102/0034654315626799 .

Košir, K., & Tement, S. (2014). Teacher–student relationship and academic achievement: A cross-lagged longitudinal study on three different age groups. European Journal of Psychology of Education , 29 (3), 409–428. https://doi.org/10.1007/s10212-013-0205-2 .

Lam, C. S., Yeung, P. P., & Yuen, M. T. (2018). Personal and environmental factors affecting Hong Kong high-ability students’ school satisfaction. Journal of Psychologists and Counselors in Schools , 28 (2), 166–184. https://doi.org/10.1017/jgc.2017.18 .

Langenkamp, I. (2018). Das akademische Selbstkonzept [The academic self-concept]. In I. Langenkamp (Ed.), Stärkung des kindlichen Selbstkonzepts [Strengthening the child’s self-concept] (pp. 25–36). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-10231-9_4 .

Lavrijsen, J., & Verschueren, K. (2020). Student characteristics affecting the recognition of high cognitive ability by teachers and peers. Learning and Individual Differences , 78 , 101820. https://doi.org/10.1016/j.lindif.2019.101820 .

Lei, H., Cui, Y., & Chiu, M. M. (2018). The relationship between teacher support and students’ academic emotions: A meta-analysis. Frontiers in Psychology , 8 , 2288. https://doi.org/10.3389/fpsyg.2017.02288 .

Lenhard, W., & Lenhard, A. (2016). Computation of effect sizes . https://www.psychometrica.de/effektstaerke.html .

Li, M. (2013). Using the propensity score method to estimate causal effects. Organizational Research Methods , 16 (2), 188–226. https://doi.org/10.1177/1094428112447816 .

Li, J., & Xue, E. (2023). Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors. Behavioral Sciences , 13 (1), 59. https://doi.org/10.3390/bs13010059 .

Likhanov, M. V., Tsigeman, E. S., Papageorgiou, K. A., Akmalov, A. F., Sabitov, I. A., & Kovas, Y. V. (2020). Ordinary extraordinary: Elusive group differences in personality and psychological difficulties between STEM-gifted adolescents and their peers. The British Journal of Educational Psychology , 91 (1), 78–100. https://doi.org/10.1111/bjep.12349

Loevinger, J. (1954). The attenuation paradox in test theory. Psychological Bulletin , 51 (5), 493–504. https://doi.org/10.1037/h0058543

Machts, N., Kaiser, J., Schmidt, F. T., & Möller, J. (2016). Accuracy of teachers’ judgments of students’ cognitive abilities: A meta-analysis. Educational Research Review , 19 , 85–103. https://doi.org/10.1016/j.edurev.2016.06.003 .

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods , 9 (4), 403–425. https://doi.org/10.1037/1082-989X.9.4.403 .

McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods , 23 (3), 412–433. https://doi.org/10.1037/met0000144 .

Merton, R. K. (1948). The self-fulfilling prophecy. The Antioch Review , 8 (2), 193–210. https://doi.org/10.2307/4609267 .

Moschner, B., & Dickhäuser, O. (2018). Selbstkonzept [Self-concept]. In D. H. Rost, J. R. Sparfeldt, & S. Buch (Eds.), Handwörterbuch pädagogische P sychologie [Handbook of educational psychology] (5th ed., pp. 750–756). Beltz.

Muthén, L. K., & Muthén, B. (1998–2019). Mplus user’s guide (Eighth ed.) . Muthén & Muthén.

Nocita, G., Perlman, M., McMullen, E., Falenchuk, O., Brunsek, A., Fletcher, B., Kamkar, N., & Shah, P. S. (2020). Early childhood specialization among ECEC educators and preschool children’s outcomes: A systematic review and meta-analysis. Early Childhood Research Quarterly , 53 , 185–207. https://doi.org/10.1016/j.ecresq.2019.10.006 .

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Peter, F., & Dalbert, C. (2010). Do my teachers treat me justly? Implications of students’ justice experience for class climate experience. Contemporary Educational Psychology , 35 (4), 297–305. https://doi.org/10.1016/j.cedpsych.2010.06.001 .

Petermann, F., & Petermann, U. (2011). Wechsler Intelligence Scale for Children - IV (WISC-IV, German edition) . Pearson.

Primi, R., Santos, D., John, O. P., & de Fruyt, F. (2021). Senna inventory for the assessment of social and emotional skills in public school students in Brazil: Measuring both identity and self-efficacy. Frontiers in Psychology , 12 , 716639. https://doi.org/10.3389/fpsyg.2021.716639 .

R Core Team. (2021). R: A language and environment for statistical computing (4.1.0) . R Foundation for Statistical Computing.

Rammstedt, B., & Beierlein, C. (2014). Can’t we make it any shorter? The limits of personality assessment and ways to overcome them. Journal of Individual Differences , 35 (4), 212–220. https://doi.org/10.1027/1614-0001/a000141 .

Rantala, T., & Määttä, K. (2012). Ten theses of the joy of learning at primary schools. Early Child Development and Care , 182 (1), 87–105. https://doi.org/10.1080/03004430.2010.545124 .

Rauer, W., & Schuck, K. D. (2003). FEESS 3–4: Fragebogen zu r Erfassung emotionaler und sozialer Schulerfahrungen v on Grundschulkindern dritter und vierter Klassen: Manual [Questionnaire to assess emotional and social school experiences of third and fourth grade elementary school children: Manual] . Beltz Test GmbH.

Rauer, W., & Schuck, K. D. (2004). FEESS 1–2: Fragebogen z ur Erfassung emotionaler und sozialer Schulerfahrungen v on Grundschulkindern erster und zweiter Klassen: Manual [Questionnaire to assess emotional and social school experiences of first and second grade elementary school children: Manual] . Beltz Test GmbH.

Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement , 21 (2), 173–184. https://doi.org/10.1177/01466216970212006 .

Ready, D. D., & Chu, E. M. (2015). Sociodemographic inequality in early literacy development: The role of teacher perceptual accuracy. Early Education & Development , 26 (7), 970–987. https://doi.org/10.1080/10409289.2015.1004516 .

Revelle, W. (2022). Procedures for psychological, psychometric, and personality research [R package psych version 2.2.9] . Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/psych/index.html .

Ridgeway, G., McCaffrey, D., Morral, A., Burgette, L., & Griffin, B. A. (2014). Toolkit for weighting and analysis of nonequivalent groups: A tutorial for the R TWANG package . RAND Corporation.

Ridgeway, G., McCaffrey, D., Morral, A., Burgette, L., & Griffin, B. A. (2015). twang: Toolkit for w eighting and a nalysis of n onequivalent g roups . Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/twang/index.html

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika , 70 (1), 41–55. https://doi.org/10.1093/biomet/70.1.41 .

Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association , 79 (387), 516–524. https://doi.org/10.2307/2288398 .

Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence , 53 , 118–137. https://doi.org/10.1016/j.intell.2015.09.002 .

Rubie-Davies, C. M., & Peterson, E. R. (2016). Relations between teachers’ achievement, over- and underestimation, and students’ beliefs for Māori and Pākehā students. Contemporary Educational Psychology , 47 , 72–83. https://doi.org/10.1016/j.cedpsych.2016.01.001 .

Rubie-Davies, C. M., Weinstein, R. S., Huang, F. L., Gregory, A., Cowan, P. A., & Cowan, C. P. (2014). Successive teacher expectation effects across the early school years. Journal of Applied Developmental Psychology , 35 (3), 181–191. https://doi.org/10.1016/j.appdev.2014.03.006 .

Rubie-Davies, C. M., Meissel, K., Alansari, M., Watson, P. W., Flint, A., & McDonald, L. (2020). Achievement and beliefs outcomes of students with high expectation teachers. Social Psychology of Education , 23 (5), 1173–1201. https://doi.org/10.1007/s11218-020-09574-y

Rucinski, C. L., Brown, J. L., & Downer, J. T. (2018). Teacher-child relationships, classroom climate, and children’s social-emotional and academic development. Journal of Educational Psychology , 110 (7), 992–1004. https://doi.org/10.1037/edu0000240 .

Rudolf, M., & Buse, J. (2020). Multivariate Verfahren: Eine praxisorientierte Einführung mit Anwendungsbeispielen [Multivariate methods: A practice-oriented introduction with application examples (3rd ed.). Hogrefe. https://doi.org/10.1026/02900-000 .

Scherrer, V., Roberts, R., & Preckel, F. (2016). Parents’ assessment of circadian preference in elementary school-aged children: Validity and relations to educational outcomes. Chronobiology International , 33 (9), 1188–1207. https://doi.org/10.1080/07420528.2016.1210618 .

Schmidt, I., Brunner, M., Keller, L., Scherrer, V., Wollschläger, R., Baudson, T. G., & Preckel, F. (2017). Profile formation of academic self-concept in elementary school students in grades 1 to 4. PloS One , 12 (5), e0177854. https://doi.org/10.1371/journal.pone.0177854 .

Sedlmeier, P., & Renkewitz, F. (2013). Forschungsmethoden und Statistik für Psychologen und Sozialwissenschaftler [Research methods and statistics for psychologists and social scientists] (2nd ed.). Pearson Deutschland. https://elibrary.pearson.de/book/99.150005/9783863266837 .

Şeker, H. (2011). Developing a questionnaire on attitude towards school. Learning Environments Research , 14 (3), 241–261. https://doi.org/10.1007/s10984-011-9096-9 .

Sheng, Y., & Sheng, Z. (2012). Is coefficient alpha robust to non-normal data? Frontiers in Psychology , 3 , 34. https://doi.org/10.3389/fpsyg.2012.00034 .

Stang, J., & Urhahne, D. (2016). Stabilität, Bezugsnormorientierung und Auswirkungen der Urteilsgenauigkeit [Stability, reference norm orientation, and effects of judgment accuracy]. Zeitschrift für Pädagogische Psychologie , 30 (4), 251–262. https://doi.org/10.1024/1010-0652/a000190

Statista (2023b). Verteilung der Lehrkräfte an allgemeinbildenden Schulen in Deutschland im Schuljahr 2021/2022 nach Altersgruppen [Distribution of teachers at general education schools in Germany in the 2021/2022 school year by age group] . https://de.statista.com/statistik/daten/studie/1129882/umfrage/verteilung-der-lehrerinnen-in-deutschland-nach-altersgruppen/ .

Statista (2023a). Anteil der weiblichen Lehrkräfte an allgemeinbildenden Schulen in Deutschland im Schuljahr 2021/2022 nach Schulart [Proportion of female teachers at general education schools in Germany in the 2021/2022 school year by school] . https://de.statista.com/statistik/daten/studie/1129852/umfrage/frauenanteil-unter-den-lehrkraeften-in-deutschland-nach-schulart/ .

Steinmayr, R., Meißner, A., Weidinger, A. F., & Wirthwein, L. (2014). Academic achievement. In L. H. Meyer (Ed.), Oxford bibliographies Online: Education . Oxford University Press.

Südkamp, A., Kaiser, J., & Möller, J. (2012). Accuracy of teachers’ judgments of students’ academic achievement: A meta-analysis. Journal of Educational Psychology , 104 (3), 743–762. https://doi.org/10.1037/a0027627 .

Thoemmes, F. J., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research , 46 (1), 90–118. https://doi.org/10.1080/00273171.2011.540475 .

Trautwein, U., & Möller, J. (2016). Self-concept: Determinants and consequences of academic self-concept in school contexts. In A. A. Lipnevich, F. Preckel, & R. D. Roberts (Eds.), The Springer Series on Human Exceptionality. Psychosocial skills and school systems in the 21st century (pp. 187–214). Springer International Publishing. https://doi.org/10.1007/978-3-319-28606-8_8 .

Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology , 7 , 769. https://doi.org/10.3389/fpsyg.2016.00769 .

Urban, J., Feuchter, M. D., Scherrer, V., Breit, M., & Preckel, F. (2023). Turn up the Heat! A Tutorial for the MAGMA R-package . https://doi.org/10.31234/osf.io/yfd7a

Urban, J., Feuchter, M. D., Scherrer, V., Breit, M., & Preckel, F. (2024). MAGMA.R: MAny-Group MAtching. R package version 1.0. https://CRAN.R-project.org/package=MAGMA.R .

Urhahne, D. (2015). Teacher behavior as a mediator of the relationship between teacherjudgment and students’ motivation and emotion. Teaching and Teacher Education, 45 , 73–82. https://doi.org/10.1016/j.tate.2014.09.006

Urhahne, D., & Wijnia, L. (2021). A review on the accuracy of teacher judgments. Educational Research Review , 32 , 100374. https://doi.org/10.1016/j.edurev.2020.100374 .

Urhahne, D., Zhou, J., Stobbe, M., Chao, S. H., Zhu, M., & Shi, J. (2010). Motivationale und affektive Merkmale unterschätzter Schüler [Motivational and affective characteristics of underestimated students]. Zeitschrift für Pädagogische Psychologie , 24 (3–4), 275–288. https://doi.org/10.1024/1010-0652/a000021

Urhahne, D., Chao, S. H., Florineth, M. L., Luttenberger, S., & Paechter, M. (2011). Academic self-concept, learning motivation, and test anxiety of the underestimated student. The British Journal of Educational Psychology , 81 (1), 161–177. https://doi.org/10.1348/000709910X504500 .

Van Rossem, R., & Vermande, M. M. (2004). Classroom roles and school adjustment. Social Psychology Quarterly , 67 (4), 396–411. https://doi.org/10.1177/019027250406700405 .

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor Package. Journal of Statistical Software , 36 (3). https://doi.org/10.18637/jss.v036.i03 .

Wahl, D., Huber, G. L., & Weinert, F. E. (2007). Psychologie für die Schulpraxis: Ein handlungsorientiertes Lehrbuch für Lehrerinnen und Lehrer [Psychology for school practice: An action-oriented textbook for teachers] (2nd ed.). Edition Sozio-Publishing: Vol. 208 . Sozio-Publishing.

Wang, S., Rubie-Davies, C. M., & Meissel, K. (2018). A systematic review of the teacher expectation literature over the past 30 years. Educational Research and Evaluation , 24 (3–5), 124–179. https://doi.org/10.1080/13803611.2018.1548798 .

Wollschläger, R. (2016). Diagnostic competencies of teachers. Accuracy of judgment, sources of bias, and consequences of (mis-)judgment [Dissertation]. University of Trier, Trier.

Xiao, L., & Hau, K. T. (2023). Performance of coefficient alpha and its alternatives: Effects of different types of non-normality. Educational and Psychological Measurement , 83 (1), 5–27. https://doi.org/10.1177/001316442210882 .

Zaboski, B. A., Kranzler, J. H., & Gage, N. A. (2018). Meta-analysis of the relationship between academic achievement and broad abilities of the Cattell-horn-Carroll theory. Journal of School Psychology , 71 , 42–56. https://doi.org/10.1016/j.jsp.2018.10.001 .

Zhou, J., & Urhahne, D. (2013). Teacher judgment, student motivation, and the mediating effect of attributions. European Journal of Psychology of Education , 28 (2), 275–295. https://doi.org/10.1007/s10212-012-0114-9 .

Ziegler, M., Kemper, C. J., & Kruyen, P. (2014). Short scales–five misunderstandings and ways to overcome them. Journal of Individual Differences , 35 (4), 185–189. https://doi.org/10.1027/1614-0001/a000148 .

Download references

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

University of Trier, Trier, Germany

Jessica Gnas, Julian Urban & Franzis Preckel

GESIS - Leibniz Institute for the Social Sciences, Mannheim, Germany

Julian Urban

LIfBi - Leibniz Institute for Educational Trajectories, Bamberg, Germany

Markus Daniel Feuchter

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jessica Gnas .

Ethics declarations

Conflict of interests.

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Gnas, J., Urban, J., Feuchter, M.D. et al. Socio-emotional experiences of primary school students: Relations to teachers’ underestimation, overestimation, or accurate judgment of their cognitive ability. Soc Psychol Educ (2024). https://doi.org/10.1007/s11218-024-09915-1

Download citation

Received : 20 July 2023

Accepted : 03 April 2024

Published : 15 May 2024

DOI : https://doi.org/10.1007/s11218-024-09915-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Teacher judgment accuracy
• Socio-emotional experiences
• Cognitive ability
• Primary school
• Propensity score matching
• Find a journal
• Publish with us
• Track your research

CREATION OF SAFE DOSE ZONES USING ISODOSE MAPS IN VIDEO FLUOROSCOPIC SWALLOWING STUDIES FOR PEDIATRIC PATIENTS

• Find this author on Google Scholar
• Find this author on PubMed
• Search for this author on this site
• ORCID record for Marco G Jimenez
• For correspondence: [email protected]
• Info/History
• Preview PDF

Introduction: During video fluoroscopic procedures in pediatric patients, a considerable number of individuals, including occupationally exposed professionals (OEP) and patient companions, must be present, exposing themselves to radiation. Objective: To determine safety areas for OEP to receive the lowest absorbed dose during the examination, as shown in isodose maps. Method: Technical parameters used in the examination were obtained by reviewing exams conducted during the years 2021 and 2022. Subsequently, a procedure was conducted to simulate these data using an anthropomorphic phantom and taking measurements at different points and levels of the examination room to create a map with dose zones in three dimensions. A Geiger Muller ionization chamber was used to measure the doses. Results: It was found that the points closest to the patient at pelvic level of the professional had the highest doses. Conclusion: OEP who must remain close to the patient have an essential obligation to comply with the use of radiological protection elements, and the relocation of elements in the room could be considered.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

ethics committee Undergraduate Scientific Alemana Clinical Medicine Faculty Desarrollo University Santiago Chile approval for this work

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present study are available upon reasonable request to the authors

View the discussion thread.

Thank you for your interest in spreading the word about medRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Citation Manager Formats

• EndNote (tagged)
• EndNote 8 (xml)
• RefWorks Tagged
• Ref Manager
• Tweet Widget
• Facebook Like
• Google Plus One
• Addiction Medicine (324)
• Allergy and Immunology (627)
• Anesthesia (163)
• Cardiovascular Medicine (2371)
• Dentistry and Oral Medicine (289)
• Dermatology (206)
• Emergency Medicine (379)
• Endocrinology (including Diabetes Mellitus and Metabolic Disease) (836)
• Epidemiology (11768)
• Forensic Medicine (10)
• Gastroenterology (702)
• Genetic and Genomic Medicine (3736)
• Geriatric Medicine (350)
• Health Economics (633)
• Health Informatics (2395)
• Health Policy (932)
• Health Systems and Quality Improvement (896)
• Hematology (341)
• HIV/AIDS (782)
• Infectious Diseases (except HIV/AIDS) (13308)
• Intensive Care and Critical Care Medicine (767)
• Medical Education (365)
• Medical Ethics (104)
• Nephrology (398)
• Neurology (3501)
• Nursing (198)
• Nutrition (524)
• Obstetrics and Gynecology (674)
• Occupational and Environmental Health (663)
• Oncology (1823)
• Ophthalmology (537)
• Orthopedics (218)
• Otolaryngology (287)
• Pain Medicine (232)
• Palliative Medicine (66)
• Pathology (446)
• Pediatrics (1033)
• Pharmacology and Therapeutics (426)
• Primary Care Research (420)
• Psychiatry and Clinical Psychology (3175)
• Public and Global Health (6138)
• Radiology and Imaging (1280)
• Rehabilitation Medicine and Physical Therapy (747)
• Respiratory Medicine (826)
• Rheumatology (379)
• Sexual and Reproductive Health (372)
• Sports Medicine (323)
• Surgery (402)
• Toxicology (50)
• Transplantation (172)
• Urology (145)

Sample Abstract - Engineering

Sensitivity analysis of dsc measurements of denaturation of a protein mixture.

Matt Ryerkerk Under the direction of Dr. Neil Wright, Mechanical Engineering

Quantifying the kinetics of denaturation of heated proteins can lead to insight into protein folding, for example. Differential scanning calorimetery (DSC) measures changes in enthalpy of a specimen as its temperature is changed. DSC is a popular method to study the kinetics of polymers and biological materials. Increasingly, researchers are using DSC to measure changes in the enthalpy of mixtures of proteins and in cells. The confidence region of the parameters reported in these studies maybe unclear, because numerous parameters are being estimated using a single enthalpy trace. The present study examines using DSC to denature rattail tendon, which is predominantly Type I collagen. Analyzing the resulting data provides values for the kinetic parameters, in particular those describing a first-order Arrhenius model, governing the reaction. Several different methods for determining the parameters have been presented in past studies. In this study, the sensitivity of the parameters to the variables of the reaction, including the method to determine the parameters, is investigated. The results can be used to as a starting point to study the reliability of parameters for DSC experiments involving the denaturation of multiple proteins.

• Call us: (517) 884-4384
• Contact Information
• Privacy Statement
• Site Accessibility
• Call MSU: (517) 355-1855
• Visit: msu.edu
• MSU is an affirmative-action, equal-opportunity employer.
• Notice of Nondiscrimination
• Spartans Will.
• © Michigan State University