Example of Journal of Educational Measurement format
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Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format
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Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format Example of Journal of Educational Measurement format
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open access Open Access

Journal of Educational Measurement — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Education #418 of 1319 down down by 83 ranks
Psychology (miscellaneous) #20 of 53 down down by 1 rank
Developmental and Educational Psychology #158 of 332 up up by 1 rank
Applied Psychology #118 of 227 up up by 5 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 115 Published Papers | 253 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 05/07/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

2.2

16% from 2019

CiteRatio for Journal of Educational Measurement from 2016 - 2020
Year Value
2020 2.2
2019 1.9
2018 1.5
2017 1.9
2016 4.3
graph view Graph view
table view Table view

1.917

64% from 2019

SJR for Journal of Educational Measurement from 2016 - 2020
Year Value
2020 1.917
2019 1.172
2018 0.922
2017 1.229
2016 2.617
graph view Graph view
table view Table view

1.811

9% from 2019

SNIP for Journal of Educational Measurement from 2016 - 2020
Year Value
2020 1.811
2019 1.665
2018 0.893
2017 0.957
2016 2.352
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 16% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 64% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Educational Measurement

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Wiley

Journal of Educational Measurement

The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field ...... Read More

Education

Psychology (miscellaneous)

Developmental and Educational Psychology

Applied Psychology

Social Sciences

i
Last updated on
05 Jul 2020
i
ISSN
0022-0655
i
Impact Factor
High - 1.532
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
apa
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Citation Type
Numbered
[25]
i
Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1111/J.1745-3984.2003.TB01108.X
Modern Multidimensional Scaling: Theory and Applications
Ingwer Borg1, Patrick J. F. Groenen1

Abstract:

Fundamentals of MDS.- The Four Purposes of Multidimensional Scaling.- Constructing MDS Representations.- MDS Models and Measures of Fit.- Three Applications of MDS.- MDS and Facet Theory.- How to Obtain Proximities.- MDS Models and Solving MDS Problems.- Matrix Algebra for MDS.- A Majorization Algorithm for Solving MDS.- Metr... Fundamentals of MDS.- The Four Purposes of Multidimensional Scaling.- Constructing MDS Representations.- MDS Models and Measures of Fit.- Three Applications of MDS.- MDS and Facet Theory.- How to Obtain Proximities.- MDS Models and Solving MDS Problems.- Matrix Algebra for MDS.- A Majorization Algorithm for Solving MDS.- Metric and Nonmetric MDS.- Confirmatory MDS.- MDS Fit Measures, Their Relations, and Some Algorithms.- Classical Scaling.- Special Solutions, Degeneracies, and Local Minima.- Unfolding.- Unfolding.- Avoiding Trivial Solutions in Unfolding.- Special Unfolding Models.- MDS Geometry as a Substantive Model.- MDS as a Psychological Model.- Scalar Products and Euclidean Distances.- Euclidean Embeddings.- MDS and Related Methods.- Procrustes Procedures.- Three-Way Procrustean Models.- Three-Way MDS Models.- Modeling Asymmetric Data.- Methods Related to MDS. read more read less

Topics:

Multidimensional scaling (55%)55% related to the paper
3,096 Citations
Journal Article DOI: 10.1111/JEDM.12000
Validating the Interpretations and Uses of Test Scores

Abstract:

To validate an interpretation or use of test scores is to evaluate the plausibility of the claims based on the scores. An argument-based approach to validation suggests that the claims based on the test scores be outlined as an argument that specifies the inferences and supporting assumptions needed to get from test responses... To validate an interpretation or use of test scores is to evaluate the plausibility of the claims based on the scores. An argument-based approach to validation suggests that the claims based on the test scores be outlined as an argument that specifies the inferences and supporting assumptions needed to get from test responses to score-based interpretations and uses. Validation then can be thought of as an evaluation of the coherence and completeness of this interpretation/use argument and of the plausibility of its inferences and assumptions. In outlining the argument-based approach to validation, this paper makes eight general points. First, it is the proposed score interpretations and uses that are validated and not the test or the test scores. Second, the validity of a proposed interpretation or use depends on how well the evidence supports the claims being made. Third, more-ambitious claims require more support than less-ambitious claims. Fourth, more-ambitious claims (e.g., construct interpretations) tend to be more useful than less-ambitious claims, but they are also harder to validate. Fifth, interpretations and uses can change over time in response to new needs and new understandings leading to changes in the evidence needed for validation. Sixth, the evaluation of score uses requires an evaluation of the consequences of the proposed uses; negative consequences can render a score use unacceptable. Seventh, the rejection of a score use does not necessarily invalidate a prior, underlying score interpretation. Eighth, the validation of the score interpretation on which a score use is based does not validate the score use. read more read less

Topics:

Test score (61%)61% related to the paper, Item response theory (51%)51% related to the paper
1,300 Citations
Journal Article DOI: 10.1111/J.1745-3984.1990.TB00754.X
Detecting Differential Item Functioning Using Logistic Regression Procedures
Hariharan Swaminathan1, H. Jane Rogers2

Abstract:

A logistic regression model for characterizing differential item functioning (DIF) between two groups is presented. A distinction is drawn between uniform and nonuniform DIF in terms of the parameters of the model. A statistic for testing the hypothesis of no DIF is developed. Through simulation studies, it is shown that the ... A logistic regression model for characterizing differential item functioning (DIF) between two groups is presented. A distinction is drawn between uniform and nonuniform DIF in terms of the parameters of the model. A statistic for testing the hypothesis of no DIF is developed. Through simulation studies, it is shown that the logistic regression procedure is more powerful than the Mantel-Haenszel procedure for detecting nonuniform DIF and as powerful in detecting uniform DIF. read more read less

Topics:

Differential item functioning (78%)78% related to the paper, Logistic regression (61%)61% related to the paper, Item response theory (59%)59% related to the paper, Regression analysis (55%)55% related to the paper, Statistic (50%)50% related to the paper
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1,023 Citations
Journal Article DOI: 10.1111/JEDM.12050
Hayes, Andrew F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, NY: The Guilford Press
Jocelyn H. Bolin1

Abstract:

Regression-based analyses remain some of the most widely employed statistical approaches throughout research and scholarly work. It is remarkable that with the same simple OLS multiple regression model, such a rich variety of research questions may be addressed, ranging from simple questions of prediction and explanation to t... Regression-based analyses remain some of the most widely employed statistical approaches throughout research and scholarly work. It is remarkable that with the same simple OLS multiple regression model, such a rich variety of research questions may be addressed, ranging from simple questions of prediction and explanation to the more complex questions such as modeling mediated or moderated relationships or even modeling of moderated mediation analyses. Hayes’s Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach presents a journey through regression-based approaches beginning with the most simple regression approaches (correlation and simple linear regression), continuing through traditional uses of regression for mediation and moderation analysis, and culminating in a discussion of conditional process analysis: a method for combining mediation and moderation into one singular analysis. The book is very readable and conversational, providing many interesting and useful examples, which will be of interest to the intermediate to advanced regression user. read more read less

Topics:

Moderated mediation (66%)66% related to the paper, Mediation (statistics) (55%)55% related to the paper, Simple linear regression (54%)54% related to the paper, Moderation (53%)53% related to the paper
817 Citations
Journal Article DOI: 10.1111/J.1745-3984.1983.TB00212.X
Rule space: an approach for dealing with misconceptions based on item response theory
Kikumi K. Tatsuoka1

Abstract:

Several authors from various disciplines such as cognitive psychology, artificial intelligence, and psychometrics have developed rule assessment methods for diagnosing students' misconceptions. Siegler's (1976, 1978) binary decision tree method was illustrated in the context of the problem of balance scales and Anderson's (19... Several authors from various disciplines such as cognitive psychology, artificial intelligence, and psychometrics have developed rule assessment methods for diagnosing students' misconceptions. Siegler's (1976, 1978) binary decision tree method was illustrated in the context of the problem of balance scales and Anderson's (1974, 1981) functional method was developed for diagnosing errors occurring during the operation of integrating functional rules. A group of artificial intelligence researchers (Brown & Burton, 1978; Brown & VanLehn, 1980; VanLehn, 1981) developed a computer program "DEBUGGY" that can diagnose a number of erroneous rules resulting from misconceptions ("bugs") in whole number subtraction problems. These investigations were motivated by an interest in the basic foundations of knowledge structure and development in human cognition. Tatsuoka and her associates (Tatsuoka, Baillie, & Yamamoto, 1982; Tatsuoka, Birenbaum, Tatsuoka, & Baillie, 1980) also developed a computer program that can diagnose a number of erroneous rules in signed-number addition and subtraction problems, but they were motivated primarily by the exploration of psychometric properties of bugs such as changes in error types or the stability of misconceptions committed by a student throughout a test (Birenbaum & Tatsuoka, 1980; Tatsuoka, 1981; Tatsuoka & Tatsuoka, 1983). Analysis of misconceptions can provide useful information in evaluating instruction or instructional materials as well as specific prescriptions for planning remediation for a student. For example, the source of many of the misconceptions committed by students is often the ambiguity of explanations or the lack of precise, accurate instructions in teaching material. It is useful to have such computer programs in educational practice. However, constructing a DEBUGGY-type system for domains more general than arithmetic is extremely difficult and time consuming. On the other hand, personal indices that are designed to summarize response patterns can be used for detecting anomalous patterns (Drasgow, 1982; Harnisch & Linn, 1981; Levine & Rubin, 1979; Sato, 1975; Tatsuoka & Tatsuoka, 1982; Wright & Stone, 1979) which may result from applications of some erroneous rules, but they have only limited power. For instance, these indices cannot diagnose sources of misconceptions or provide prescriptive information for remediating them. Nevertheless, the use of personal indices is economical and applicable to general areas because such indices can first be used to spot candidates among students who may possibly possess misconceptions and may hence need personal attention from teachers. This procedure seems to be widely used in Japan (Sato, 1982). This study develops a model to fill the gap between the rule-assessing methods and the personal-index approach. The number of different erroneous rules already discovered in read more read less
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800 Citations
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Frequently asked questions

1. Can I write Journal of Educational Measurement in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Educational Measurement guidelines and auto format it.

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Yes, the template is compliant with the Journal of Educational Measurement guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Educational Measurement?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Educational Measurement citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Educational Measurement.

5. Can I use a manuscript in Journal of Educational Measurement that I have written in MS Word?

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After writing your paper autoformatting in Journal of Educational Measurement, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Educational Measurement's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Educational Measurement?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Educational Measurement. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Educational Measurement?

The 5 most common citation types in order of usage for Journal of Educational Measurement are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Educational Measurement Endnote style according to Elsevier guidelines.

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