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Sutherland, Kevin S.; Conroy, Maureen A.; Algina, James; Ladwig, Crystal; Jesse, Gabriel; Gyure, Maria – Grantee Submission, 2018
Research has consistently linked early problem behavior with later adjustment problems, including antisocial behavior, learning problems and risk for the development of emotional/behavioral disorders (EBDs). Researchers have focused upon developing effective intervention programs for young children who arrive in preschool exhibiting chronic…
Descriptors: Child Behavior, Behavior Problems, Teacher Student Relationship, Interaction
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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
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Keselman, H. J.; Algina, James; Lix, Lisa M.; Wilcox, Rand R.; Deering, Kathleen N. – Psychological Methods, 2008
Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of…
Descriptors: Intervals, Testing, Least Squares Statistics, Effect Size
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Coombs, William T.; Algina, James – Journal of Educational and Behavioral Statistics, 1996
Type I error rates for the Johansen test were estimated using simulated data for a variety of conditions. Results indicate that Type I error rates for the Johansen test depend heavily on the number of groups and the ratio of the smallest sample size to the number of dependent variables. Sample size guidelines are presented. (SLD)
Descriptors: Group Membership, Hypothesis Testing, Multivariate Analysis, Robustness (Statistics)
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Algina, James; Swaminathan, Hariharan – Journal of Experimental Education, 1977
One of the most frequently encountered problems in educational research and evaluation is that of evaluating the effect of a treatment in settings over which the researcher or evaluator has little control. A strong plea is made for use of quasi-experimental designs in these situations and this research provides a method of testing the hypothesis…
Descriptors: Educational Research, Experimental Groups, Hypothesis Testing, Research Methodology
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Algina, James – Multivariate Behavioral Research, 1982
The use of analysis of covariance in simple repeated measures designs is considered. Conditions necessary for the analysis of covariance adjusted main effects and interactions to be meaningful are presented. (Author/JKS)
Descriptors: Analysis of Covariance, Analysis of Variance, Data Analysis, Hypothesis Testing
Olejnik, Stephen F.; Algina, James – 1985
The present investigation developed power curves for two parametric and two nonparametric procedures for testing the equality of population variances. Both normal and non-normal distributions were considered for the two group design with equal and unequal sample frequencies. The results indicated that when population distributions differed only in…
Descriptors: Computer Simulation, Hypothesis Testing, Power (Statistics), Sampling
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Algina, James; Olejnik, Stephen F. – Educational and Psychological Measurement, 1984
The Welch-James procedure may be used to test hypothesis on means, when independent samples from populations with heterogenous variances are available. Summation formulas for the Welch-James procedure are presented for the 2x2 design. Matrix formulas that permit routine application of the procedure to crossed factorial designs are presented.…
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Formulas, Matrices
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Olejnik, Stephen F.; Algina, James – Journal of Educational Statistics, 1984
Using computer simulation, parametric analysis of covariance (ANCOVA) was compared to ANCOVA with data transformed using ranks, in terms of proportion of Type I errors and statistical power. Results indicated that parametric ANCOVA was robust to violations of either normality or homoscedasticity, but practiced significant power differences favored…
Descriptors: Analysis of Covariance, Computer Simulation, Hypothesis Testing, Nonparametric Statistics
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Algina, James; Olejnik, Stephen F. – Evaluation Review, 1982
A method is presented for analyzing data collected in a multiple group time-series design. This consists of testing linear hypotheses about the experimental and control group-means. Both a multivariate and a univariate procedure are described. (Author/GK)
Descriptors: Control Groups, Data Analysis, Evaluation Methods, Experimental Groups
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Algina, James – Multivariate Behavioral Research, 1994
Alternative tests are presented for the between-by-within interaction null hypothesis and for two within-subjects main effects null hypothesis in a split plot design. Estimated Type I error rates for the interaction tests and for several tests of the second null hypothesis are reported. (SLD)
Descriptors: Equations (Mathematics), Error of Measurement, Estimation (Mathematics), Hypothesis Testing
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Moulder, Bradley C.; Algina, James – Structural Equation Modeling, 2002
Used simulation to compare structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables. Findings indicate that the two-stage least squares procedure exhibited more bias and lower power than the other methods. The Jaccard-Wan procedure (J. Jaccard and C. Wan, 1995) and maximum…
Descriptors: Comparative Analysis, Estimation (Mathematics), Hypothesis Testing, Least Squares Statistics
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Olejnik, Stephen F.; Algina, James – 1984
Five distribution-free alternatives to parametric analysis of covariance (ANCOVA) are presented and demonstrated using a specific data example. The procedures considered are those suggested by Quade (1967); Puri and Sen (1969); McSweeney and Porter (1971); Burnett and Barr (1978); and Shirley (1981). The results of simulation studies investigating…
Descriptors: Analysis of Covariance, Comparative Analysis, Hypothesis Testing, Mathematical Formulas
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Olejnik, Stephen F.; Algina, James – Evaluation Review, 1985
Five distribution-free alternatives to parametric analysis of covariance are presented and demonstrated: Quade's distribution-free test, Puri and Sen's solution, McSweeney and Porter's rank transformation, Burnett and Barr's rank difference scores, and Shirley's general linear model solution. The results of simulation studies regarding Type I…
Descriptors: Analysis of Covariance, Comparative Analysis, Hypothesis Testing, Monte Carlo Methods
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Olejnik, Stephen F.; Algina, James – 1985
This paper examined the rank transformation approach to analysis of variance as a solution to the Behrens-Fisher problem. Using simulation methodology four parameters were manipulated for the two group design: (1) ratio of population variances; (2) distribution form; (3) sample size and (4) population mean difference. The results indicated that…
Descriptors: Analysis of Variance, Computer Simulation, Error of Measurement, Hypothesis Testing