Educational and Psychological Measurement (EDUC PSYCHOL MEAS)

Publisher: American College Personnel Association; Science Research Associates, SAGE Publications

Journal description

Educational and Psychological Measurement publishes data-based studies in educational measurement, as well as theoretical papers in the measurement field. The journal focuses on discussions of problems in measurement of individual differences, as well as research on the development and use of tests and measurement in education, psychology, industry and government.

Current impact factor: 1.15

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 1.154
2013 Impact Factor 1.167
2012 Impact Factor 1.07
2011 Impact Factor 1.158
2010 Impact Factor 0.831
2009 Impact Factor 0.633
2008 Impact Factor 0.872
2007 Impact Factor 0.831
2006 Impact Factor 0.921
2005 Impact Factor 0.773
2004 Impact Factor 0.756
2003 Impact Factor 0.815
2002 Impact Factor 1.661
2001 Impact Factor 0.789
2000 Impact Factor 0.608
1999 Impact Factor 0.623
1998 Impact Factor 0.618
1997 Impact Factor 0.444
1996 Impact Factor 0.316
1995 Impact Factor 0.317
1994 Impact Factor 0.368
1993 Impact Factor 0.228
1992 Impact Factor 0.324

Impact factor over time

Impact factor
Year

Additional details

5-year impact 1.51
Cited half-life >10.0
Immediacy index 0.23
Eigenfactor 0.00
Article influence 0.89
Website Educational and Psychological Measurement website
Other titles Educational and psychological measurement, EPM
ISSN 0013-1644
OCLC 1567567
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

SAGE Publications

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Authors retain copyright
    • Pre-print on any website
    • Author's post-print on author's personal website, departmental website, institutional website or institutional repository
    • On other repositories including PubMed Central after 12 months embargo
    • Publisher copyright and source must be acknowledged
    • Publisher's version/PDF cannot be used
    • Post-print version with changes from referees comments can be used
    • "as published" final version with layout and copy-editing changes cannot be archived but can be used on secure institutional intranet
    • Must link to publisher version with DOI
    • Publisher last reviewed on 29/07/2015
  • Classification
    green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: This study examined the predictors and psychometric outcomes of survey satisficing, wherein respondents provide quick, “good enough” answers (satisficing) rather than carefully considered answers (optimizing). We administered surveys to university students and respondents—half of whom held college degrees—from a for-pay survey website, and we used an experimental method to randomly assign the participants to survey formats, which presumably differed in task difficulty. Based on satisficing theory, we predicted that ability, motivation, and task difficulty would predict satisficing behavior and that satisficing would artificially inflate internal consistency reliability and both convergent and discriminant validity correlations. Indeed, results indicated effects for task difficulty and motivation in predicting survey satisficing, and satisficing in the first part of the study was associated with improved internal consistency reliability and convergent validity but also worse discriminant validity in the second part of the study. Implications for research designs and improvements are discussed.
    No preview · Article · Jan 2016 · Educational and Psychological Measurement

  • No preview · Article · Jan 2016 · Educational and Psychological Measurement
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories.
    No preview · Article · Dec 2015 · Educational and Psychological Measurement
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    ABSTRACT: Differential item functioning (DIF) indicates the violation of the invariance assumption, for instance, in models based on item response theory (IRT). For item-wise DIF analysis using IRT, a common metric for the item parameters of the groups that are to be compared (e.g., for the reference and the focal group) is necessary. In the Rasch model, therefore, the same linear restriction is imposed in both groups. Items in the restriction are termed the ``anchor items''. Ideally, these items are DIF-free to avoid artificially augmented false alarm rates. However, the question how DIF-free anchor items are selected appropriately is still a major challenge. Furthermore, various authors point out the lack of new anchor selection strategies and the lack of a comprehensive study especially for dichotomous IRT models. This article reviews existing anchor selection strategies that do not require any knowledge prior to DIF analysis, offers a straightforward notation, and proposes three new anchor selection strategies. An extensive simulation study is conducted to compare the performance of the anchor selection strategies. The results show that an appropriate anchor selection is crucial for suitable item-wise DIF analysis. The newly suggested anchor selection strategies outperform the existing strategies and can reliably locate a suitable anchor when the sample sizes are large enough.
    No preview · Article · Dec 2015 · Educational and Psychological Measurement

  • No preview · Article · Nov 2015 · Educational and Psychological Measurement

  • No preview · Article · Nov 2015 · Educational and Psychological Measurement
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    ABSTRACT: A method for evaluating the validity of multicomponent measurement instruments in heterogeneous populations is discussed. The procedure can be used for point and interval estimation of criterion validity of linear composites in populations representing mixtures of an unknown number of latent classes. The approach permits also the evaluation of between-class validity differences as well as within-class validity coefficients. The method can similarly be used with known class membership when distinct populations are investigated, their number is known beforehand and membership in them is observed for the studied subjects, as well as in settings where only the number of latent classes is known. The discussed procedure is illustrated with numerical data.
    No preview · Article · Nov 2015 · Educational and Psychological Measurement
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    ABSTRACT: Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and posttest scores that are most often used in MLM are total scores. In prior research, there have been concerns regarding measurement error in the use of total scores in using MLM. In this article, using ordinary least squares and an attenuation formula, we derive the measurement error correction formula for cluster-level group difference estimates from MLM in the presence of measurement error in the outcome, the covariate, or both. Examples are provided to illustrate the correction formula in cluster randomized and observational studies using between-cluster reliability coefficients recently developed.
    No preview · Article · Oct 2015 · Educational and Psychological Measurement

  • No preview · Article · Oct 2015 · Educational and Psychological Measurement
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    ABSTRACT: Standard approaches for estimating item response theory (IRT) model parameters generally work under the assumption that the latent trait being measured by a set of items follows the normal distribution. Estimation of IRT parameters in the presence of nonnormal latent traits has been shown to generate biased person and item parameter estimates. A number of methods, including Ramsay curve item response theory, have been developed to reduce such bias, and have been shown to work well for relatively large samples and long assessments. An alternative approach to the nonnormal latent trait and IRT parameter estimation problem, nonparametric Bayesian estimation approach, has recently been introduced into the literature. Very early work with this method has shown that it could be an excellent option for use when fitting the Rasch model when assumptions cannot be made about the distribution of the model parameters. The current simulation study was designed to extend research in this area by expanding the simulation conditions under which it is examined and to compare the nonparametric Bayesian estimation approach to the Ramsay curve item response theory, marginal maximum likelihood, maximum a posteriori, and the Bayesian Markov chain Monte Carlo estimation method. Results of the current study support that the nonparametric Bayesian estimation approach may be a preferred option when fitting a Rasch model in the presence of nonnormal latent traits and item difficulties, as it proved to be most accurate in virtually all scenarios that were simulated in this study.
    No preview · Article · Oct 2015 · Educational and Psychological Measurement
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    ABSTRACT: A latent variable modeling approach for scale reliability evaluation in heterogeneous populations is discussed. The method can be used for point and interval estimation of reliability of multicomponent measuring instruments in populations representing mixtures of an unknown number of latent classes or subpopulations. The procedure is helpful also for evaluation of possible between-class reliability differences as well as of within-class reliability coefficients. The estimation approach can similarly be used in empirical settings with known class membership when distinct populations are investigated, their number is known beforehand and membership in them is observed for the studied subjects, or alternatively in settings where only the number of latent classes is known. A modification and extension of the method for evaluation of maximal reliability or coefficient alpha in heterogeneous populations are also outlined. The discussed procedure is illustrated with numerical data.
    No preview · Article · Oct 2015 · Educational and Psychological Measurement
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    ABSTRACT: Several studies have stressed the importance of simultaneously estimating interaction and quadratic effects in multiple regression analyses, even if theory only suggests an interaction effect should be present. Specifically, past studies suggested that failing to simultaneously include quadratic effects when testing for interaction effects could result in Type I errors, Type II errors, or misleading interactions. Research investigating this issue has been limited to multiple regression models. Contrarily, structural equation modeling is a more appropriate analysis when hypotheses include latent variables. The current study utilized Monte Carlo simulation to investigate whether quadratic effects should be included in the latent variable interaction model. Consistent with previous research, it was found that including latent variable quadratic effects in the model successfully reduced the frequency of spurious interaction effects but at a cost of low power to detect true interaction effects, inaccurate parameter estimates, inaccurate standard error estimates, and reduced convergence rates. Based on findings from the current study, we recommend that researchers hypothesizing interactions between latent variables should test for these relations using the latent variable interaction model rather than the interaction quadratic model. If researchers are concerned about spurious interactions, then they may want to consider including quadratic effects in the model, provided that they have sample sizes of at least 500 and high indicator reliability. We caution all researchers to base higher order effects models on theory.
    No preview · Article · Oct 2015 · Educational and Psychological Measurement