Evidence map›Paper›PMID 39757139›Full record

ArticleAnatomical sciences education2025

Differences in motivation for biology learning: A measurement invariance testing and latent mean comparison approach.

Ting Dai, Kuan Xing, Avi Kaplan, Jennifer G Cromley, Kyle R Mara, Tony Perez

Abstract readComparative Study
In one paragraph

Article in Anatomical sciences education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ting DaiDepartment of Educational Psychology, University of Illinois Chicago, Chicago, Illinois, USA.ORCID https://orcid.org/0000-0001-8875-9983
Kuan XingFamily Medicine/OCRME, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.
Avi KaplanDepartment of Psychological Studies in Education, Temple University, Philadelphia, Pennsylvania, USA.
Jennifer G CromleyDepartment of Educational Psychology, University of Illinois Urbana-Champaign, Champaign, Illinois, USA.
Kyle R MaraDepartment of Biology, University of Southern Indiana, Evansville, Indiana, USA.
Tony PerezDepartment of Educational Foundations & Leadership, Old Dominion University, Norfolk, Virginia, USA.

Funding

Institute of Education Sciences R305A140602
6 · The paper itself

Abstract

Educational and psychological research often involves comparing motivation across groups. It is critical to ensure that observed differences in motivation are true variations by group, not due to measurement biases. With a diverse sample of undergraduate students (N = 2200), this study measured internal consistency and gathered validity evidence based on the internal structure of five motivation scales. To compare motivation for biology between groups of undergraduate students, this study tested for measurement scalar invariance by group and, accordingly, conducted latent factor mean comparisons to understand true group differences. On average, female students held lower expectancy beliefs and self-efficacy for biology learning than males. Female students perceived higher attainment value and utility value for biology learning and higher psychological cost. First-generation college students held lower expectancy beliefs and self-efficacy but perceived higher attainment value for biology learning than continuing-generation students. No differences in average motivation for biology learning were found between underrepresented racial minority (URM) and non-URM students. The implications of these findings and future research directions are also discussed.

Indexed as

BiologyLearningMotivationStudentsAdolescentAdultFemaleHumansMaleMinority GroupsSelf EfficacySex FactorsSurveys and QuestionnairesUniversitiesYoung Adultacademic motivationachievement motivationconfirmatory factor analysisfirst‐generation college studentsgroup differencesmeasurement invarianceSTEM motivation

Identifiers

PMID39757139
PMCPMC12319386

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.