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Wu, Tong; Kim, Stella Y.; Westine, Carl – Educational and Psychological Measurement, 2023
For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used…
Descriptors: Data Analysis, Responses, Statistical Analysis, Measurement
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Goretzko, David; Heumann, Christian; Bühner, Markus – Educational and Psychological Measurement, 2020
Exploratory factor analysis is a statistical method commonly used in psychological research to investigate latent variables and to develop questionnaires. Although such self-report questionnaires are prone to missing values, there is not much literature on this topic with regard to exploratory factor analysis--and especially the process of factor…
Descriptors: Factor Analysis, Data Analysis, Research Methodology, Psychological Studies
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Wiens, Stefan; Nilsson, Mats E. – Educational and Psychological Measurement, 2017
Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful…
Descriptors: Data Analysis, Effect Size, Computation, Statistical Analysis
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Huang, Francis L. – Educational and Psychological Measurement, 2018
Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials…
Descriptors: Multivariate Analysis, Sampling, Statistical Inference, Data Analysis
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Raykov, Tenko; Marcoulides, George A. – Educational and Psychological Measurement, 2014
This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably…
Descriptors: Data Analysis, Identification, Maximum Likelihood Statistics, Statistical Analysis
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Thomas, D. Roland; Zumbo, Bruno D. – Educational and Psychological Measurement, 2012
There is such doubt in research practice about the reliability of difference scores that granting agencies, journal editors, reviewers, and committees of graduate students' theses have been known to deplore their use. This most maligned index can be used in studies of change, growth, or perhaps discrepancy between two measures taken on the same…
Descriptors: Statistical Analysis, Reliability, Scores, Change
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Gómez-Benito, Juana; Hidalgo, Maria Dolores; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013
The objective of this article was to find an optimal decision rule for identifying polytomous items with large or moderate amounts of differential functioning. The effectiveness of combining statistical tests with effect size measures was assessed using logistic discriminant function analysis and two effect size measures: R[superscript 2] and…
Descriptors: Item Analysis, Test Items, Effect Size, Statistical Analysis
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Svetina, Dubravka – Educational and Psychological Measurement, 2013
The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in noncompensatory multidimensional item response models using dimensionality assessment procedures based on DETECT (dimensionality evaluation to enumerate contributing traits) and NOHARM (normal ogive harmonic analysis robust method). Five…
Descriptors: Item Response Theory, Statistical Analysis, Computation, Test Length
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Shear, Benjamin R.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013
Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…
Descriptors: Error of Measurement, Multiple Regression Analysis, Data Analysis, Computer Simulation
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Finch, W. Holmes – Educational and Psychological Measurement, 2011
Missing information is a ubiquitous aspect of data analysis, including responses to items on cognitive and affective instruments. Although the broader statistical literature describes missing data methods, relatively little work has focused on this issue in the context of differential item functioning (DIF) detection. Such prior research has…
Descriptors: Test Bias, Data Analysis, Item Response Theory, Regression (Statistics)
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Sueiro, Manuel J.; Abad, Francisco J. – Educational and Psychological Measurement, 2011
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…
Descriptors: Goodness of Fit, Item Response Theory, Nonparametric Statistics, Probability
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Cho, Sun-Joo; Li, Feiming; Bandalos, Deborah – Educational and Psychological Measurement, 2009
The purpose of this study was to investigate the application of the parallel analysis (PA) method for choosing the number of factors in component analysis for situations in which data are dichotomous or ordinal. Although polychoric correlations are sometimes used as input for component analyses, the random data matrices generated for use in PA…
Descriptors: Correlation, Evaluation Methods, Data Analysis, Matrices
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Van Fleet, David D.; Chamberlain, Howard – Educational and Psychological Measurement, 1979
This paper presents an empirical analysis of similarities and differences between two statistics, G and Phi, which treat genuinely dichotomous data. These results can aid researchers in selecting between these two statistics and in evaluating results from the use of one v the other. (Author)
Descriptors: Correlation, Data Analysis, Goodness of Fit, Nonparametric Statistics
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Elshout, Jan; And Others – Educational and Psychological Measurement, 1979
It has been shown that the degree of restriction of range taken into account in testing the hypothesis that rho equals zero, entails risks of incorrect inferences. It is argued that an alternative is to disregard the restriction of range and to use the common t-statistics proposed by regression theory. (Author/JKS)
Descriptors: Correlation, Data Analysis, Hypothesis Testing, Multiple Regression Analysis
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McQuitty, Louis L. – Educational and Psychological Measurement, 1971
Descriptors: Classification, Cluster Analysis, Comparative Analysis, Data Analysis
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