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Butler, Olivia D.; Hanson, Bradley A. – 1997
The effectiveness of smoothing in reducing random errors in equipercentile equating of a short writing assessment with two raters, two prompts, with scores ranging from zero to five was examined. Thirteen methods were examined: no equating, three presmoothing, three postsmoothing, three combination presmoothing and postsmoothing, mean equating,…
Descriptors: Equated Scores, Sample Size, Test Results, Writing Tests
Hess, Brian; Olejnik, Stephen; Huberty, Carl J. – 2001
The efficacy of two improvement-over-chance or "I" effect sizes, derived from predictive discriminant analysis (PDA) and logistic regression analysis (LRA), were investigated for two-group univariate mean comparisons. Data were generated under selected levels of population separation, variance pattern, sample size, and distribution…
Descriptors: Comparative Analysis, Effect Size, Regression (Statistics), Sample Size
Brooks, Gordon P.; Kanyongo, Gibbs Y.; Kyei-Blankson, Lydia; Gocmen, Gulsah – 2002
Unfortunately, researchers do not usually have measurement instruments that provide perfectly reliable scores. Therefore, the researcher may want to account for the level of unreliability by appropriately increasing the sample size. For example, the results of a pilot study may indicate that a particular instrument is not as reliable with a given…
Descriptors: Analysis of Variance, Correlation, Reliability, Sample Size
Swaminathan, Hariharan; Hambleton, Ronald K.; Sireci, Stephen G.; Xing, Dehui; Rizavi, Saba M. – 2003
The primary objective of this study was to investigate how incorporating prior information improves estimation of item parameters in two small samples. The factors that were investigated were sample size and the type of prior information. To investigate the accuracy with which item parameters in the Law School Admission Test (LSAT) are estimated,…
Descriptors: Estimation (Mathematics), Item Response Theory, Sample Size, Sampling
Kieffer, Kevin M.; Thompson, Bruce – 1999
As the 1994 publication manual of the American Psychological Association emphasized, "p" values are affected by sample size. As a result, it can be helpful to interpret the results of statistical significant tests in a sample size context by conducting so-called "what if" analyses. However, these methods can be inaccurate…
Descriptors: Educational Research, Sample Size, Statistical Significance, Test Interpretation
Chang, Shun-Wen; Hanson, Bradley A.; Harris, Deborah J. – 2000
This study presents and evaluates a method of standardization that may be used by test practitioners to standardize classical item statistics when sample sizes are small. The effectiveness of this standardization approach was compared through simulation with the one-parameter logistic (1PL) and three parameter logistic (3PL) models based on the…
Descriptors: Item Response Theory, Sample Size, Simulation, Statistical Analysis
Peer reviewed Peer reviewed
Strube, Michael J. – Journal of Consulting and Clinical Psychology, 1991
Demonstrates low probability that nonequivalence will produce erroneous inferences in small samples. Sees probability of erroneous inference in absence of true treatment effect as generally no greater than nominal Type I error rate. Seems unlikely that small samples have biased inferences drawn from past psychotherapy outcome research. Cites other…
Descriptors: Outcomes of Treatment, Psychotherapy, Research Problems, Sample Size
Peer reviewed Peer reviewed
Carter, Robert T.; Swanson, Jane L. – Journal of Vocational Behavior, 1990
Reviews research literature to determine psychometric validity of Strong Interest Inventory with Black samples. Found only eight relevant studies. Results found little evidence of Strong's psychometric validity with Black samples. (Author/CM)
Descriptors: Blacks, Psychological Evaluation, Psychometrics, Sample Size
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Wollack, James A.; Cohen, Allan S. – Applied Psychological Measurement, 1998
Investigated empirical Type I error rates and the power of omega (index of answer copying developed by J. Wollack, 1997) when item and trait (theta) parameters were unknown and estimated from datasets of 100 and 500 examinees. Type I error was unaffected by estimating item parameters, with power slightly lower for the smaller sample. (SLD)
Descriptors: Cheating, Estimation (Mathematics), Plagiarism, Sample Size
Peer reviewed Peer reviewed
Janes, Joseph – Library Hi Tech, 2000
Continues a series on topics in research methodology, statistics, and data analysis techniques for the library and information sciences. Focuses on the basics of sampling for surveys or experimental work, including rationale, terminology, technique, alternative methods, and sample size. (Author/LRW)
Descriptors: Library Research, Research Methodology, Sample Size, Sampling
Peer reviewed Peer reviewed
Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling, 2000
Outlines a method for comparing completely standardized solutions in multiple groups. The method is based on a correlation structure analysis of equal-size samples and uses the correlation distribution theory implemented in the structural equation modeling program RAMONA. (SLD)
Descriptors: Comparative Analysis, Correlation, Sample Size, Structural Equation Models
Peer reviewed Peer reviewed
Bonett, Douglas G.; Wright, Thomas A. – Psychometrika, 2000
Reviews interval estimates of the Pearson, Kendall tau-alpha, and Spearman correlates and proposes an improved standard error for the Spearman correlation. Examines the sample size required to yield a confidence interval having the desired width. Findings show accurate results from a two-stage approximation to the sample size. (SLD)
Descriptors: Correlation, Error of Measurement, Estimation (Mathematics), Sample Size
Peer reviewed Peer reviewed
Algina, James – Educational and Psychological Measurement, 1998
In this study, the Statistical Analysis System (SAS) was used to program power calculations for multiple comparisons of two groups on "p" variables. Three programs were prepared to accommodate variations in the information that may be available to do the power analysis. (Author/SLD)
Descriptors: Comparative Analysis, Computer Software, Power (Statistics), Sample Size
Peer reviewed Peer reviewed
Muniz, Jose; Hambleton, Ronald K.; Xing, Dehui – International Journal of Testing, 2001
Studied two procedures for detecting potentially flawed items in translated tests with small samples: (1) conditional item "p" value comparisons; and (2) delta plots. Varied several factors in this simulation study. Findings show that the two procedures can be valuable in identifying flawed test items, especially when the size of the…
Descriptors: Identification, Sample Size, Simulation, Test Items
Peer reviewed Peer reviewed
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Kim, Kevin H. – Structural Equation Modeling, 2005
The relation among fit indexes, power, and sample size in structural equation modeling is examined. The noncentrality parameter is required to compute power. The 2 existing methods of computing power have estimated the noncentrality parameter by specifying an alternative hypothesis or alternative fit. These methods cannot be implemented easily and…
Descriptors: Structural Equation Models, Sample Size, Goodness of Fit
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