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Crooks, Noelle M.; Bartel, Anna N.; Alibali, Martha W. – Statistics Education Research Journal, 2019
In recent years, there have been calls for researchers to report and interpret confidence intervals (CIs) rather than relying solely on p-values. Such reforms, however, may be hindered by a general lack of understanding of CIs and how to interpret them. In this study, we assessed conceptual knowledge of CIs in undergraduate and graduate psychology…
Descriptors: Undergraduate Students, Graduate Students, Psychology, Statistics
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Crooks, Noelle M.; Bartel, Anna N.; Alibali, Martha W. – Grantee Submission, 2019
In recent years, there have been calls for researchers to report and interpret confidence intervals (CIs) rather than relying solely on p-values. Such reforms, however, may be hindered by a general lack of understanding of CIs and how to interpret them. In this study, we assessed conceptual knowledge of CIs in undergraduate and graduate psychology…
Descriptors: Undergraduate Students, Graduate Students, Psychology, Statistics
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Alards-Tomalin, Doug; Leboe-McGowan, Jason P.; Shaw, Joshua D. M.; Leboe-McGowan, Launa C. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014
The relative magnitude (or intensity) of an event can have direct implications on timing estimation. Previous studies have found that greater magnitude stimuli are often reported as longer in duration than lesser magnitudes, including Arabic digits (Xuan, Zhang, He, & Chen, 2007). One explanation for these findings is that different…
Descriptors: Computation, Intervals, Time, Visual Stimuli
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Doebler, Anna; Doebler, Philipp; Holling, Heinz – Psychometrika, 2013
The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter [theta] is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given…
Descriptors: Foreign Countries, Item Response Theory, Computation, Hypothesis Testing
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Woods, Carol M. – Multivariate Behavioral Research, 2009
Gamma-family measures are bivariate ordinal correlation measures that form a family because they all reduce to Goodman and Kruskal's gamma in the absence of ties (1954). For several gamma-family indices, more than one variance estimator has been introduced. In previous research, the "consistent" variance estimator described by Cliff and…
Descriptors: Intervals, Computation, Evaluation, Simulation
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Sass, Daniel A. – Educational and Psychological Measurement, 2010
Exploratory factor analysis (EFA) is commonly employed to evaluate the factor structure of measures with dichotomously scored items. Generally, only the estimated factor loadings are provided with no reference to significance tests, confidence intervals, and/or estimated factor loading standard errors. This simulation study assessed factor loading…
Descriptors: Intervals, Simulation, Factor Structure, Hypothesis Testing
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Krishnamoorthy, K.; Xia, Yanping – Multivariate Behavioral Research, 2008
The problems of hypothesis testing and interval estimation of the squared multiple correlation coefficient of a multivariate normal distribution are considered. It is shown that available one-sided tests are uniformly most powerful, and the one-sided confidence intervals are uniformly most accurate. An exact method of calculating sample size to…
Descriptors: Statistical Analysis, Intervals, Sample Size, Testing
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Cumming, Geoff – Teaching Statistics: An International Journal for Teachers, 2007
A picture of a 95% confidence interval (CI) implicitly contains pictures of CIs of all other levels of confidence, and information about the "p"-value for testing a null hypothesis. This article discusses pictures, taken from interactive software, that suggest several ways to think about the level of confidence of a CI,…
Descriptors: Intervals, Statistics, Computation, Visual Aids
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research