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Reichardt, Charles S. – Multivariate Behavioral Research, 2011
Maxwell, Cole, and Mitchell (2011) demonstrated that simple structural equation models, when used with cross-sectional data, generally produce biased estimates of meditated effects. I extend those results by showing how simple structural equation models can produce biased estimates of meditated effects when used even with longitudinal data. Even…
Descriptors: Structural Equation Models, Statistical Data, Longitudinal Studies, Error of Measurement
Hamelin, Jeffery P.; Frijters, Jan; Griffiths, Dorothy; Condillac, Rosemary; Owen, Frances – Journal of Intellectual & Developmental Disability, 2011
Background: A meta-analysis examined the effects of deinstitutionalisation on adaptive behaviour outcomes in persons with intellectual disability. The need for an updated review in this area is reflected by recent policy shifts in community care practices and the international status of deinstitutionalisation efforts. Method: Twenty-three studies…
Descriptors: Community Services, Research Design, Sample Size, Mental Retardation
Chan, Wai – Educational and Psychological Measurement, 2009
A typical question in multiple regression analysis is to determine if a set of predictors gives the same degree of predictor power in two different populations. Olkin and Finn (1995) proposed two asymptotic-based methods for testing the equality of two population squared multiple correlations, [rho][superscript 2][subscript 1] and…
Descriptors: Multiple Regression Analysis, Intervals, Correlation, Computation
Eisenhauer, Joseph G. – Teaching Statistics: An International Journal for Teachers, 2009
Very little explanatory power is required in order for regressions to exhibit statistical significance. This article discusses some of the causes and implications. (Contains 2 tables.)
Descriptors: Statistical Significance, Educational Research, Sample Size, Probability
Algina, James; Keselman, Harvey J.; Penfield, Randall J. – Educational and Psychological Measurement, 2008
A squared semipartial correlation coefficient ([Delta]R[superscript 2]) is the increase in the squared multiple correlation coefficient that occurs when a predictor is added to a multiple regression model. Prior research has shown that coverage probability for a confidence interval constructed by using a modified percentile bootstrap method with…
Descriptors: Intervals, Correlation, Probability, Multiple Regression Analysis
Algina, James; Keselman, H. J. – Educational and Psychological Measurement, 2008
Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection. (Contains 2 tables.)
Descriptors: Intervals, Sample Size, Validity, Hypothesis Testing
Jance, Marsha; Thomopoulos, Nick – American Journal of Business Education, 2009
The extreme interval values and statistics (expected value, median, mode, standard deviation, and coefficient of variation) for the smallest (min) and largest (max) values of exponentially distributed variables with parameter ? = 1 are examined for different observation (sample) sizes. An extreme interval value g[subscript a] is defined as a…
Descriptors: Intervals, Statistics, Predictor Variables, Sample Size
Maydeu-Olivares, Alberto; Coffman, Donna L.; Hartmann, Wolfgang M. – Psychological Methods, 2007
The point estimate of sample coefficient alpha may provide a misleading impression of the reliability of the test score. Because sample coefficient alpha is consistently biased downward, it is more likely to yield a misleading impression of poor reliability. The magnitude of the bias is greatest precisely when the variability of sample alpha is…
Descriptors: Intervals, Scores, Sample Size, Simulation
Cheung, Mike W. L. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
Mediators are variables that explain the association between an independent variable and a dependent variable. Structural equation modeling (SEM) is widely used to test models with mediating effects. This article illustrates how to construct confidence intervals (CIs) of the mediating effects for a variety of models in SEM. Specifically, mediating…
Descriptors: Structural Equation Models, Probability, Intervals, Sample Size
Strang, Kenneth David – Practical Assessment, Research & Evaluation, 2009
This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…
Descriptors: Multicultural Education, Computer Software, Multiple Regression Analysis, Multidimensional Scaling
Shieh, Gwowen – Psychometrika, 2006
This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…
Descriptors: Intervals, Sample Size, Correlation, Computation
Bonett, Douglas G.; Price, Robert M. – Journal of Educational and Behavioral Statistics, 2005
The tetrachoric correlation describes the linear relation between two continuous variables that have each been measured on a dichotomous scale. The treatment of the point estimate, standard error, interval estimate, and sample size requirement for the tetrachoric correlation is cursory and incomplete in modern psychometric and behavioral…
Descriptors: Correlation, Predictor Variables, Measures (Individuals), Error of Measurement