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Bonacich, Phillip; Bienenstock, Elisa Jayne – Social Psychology Quarterly, 2009
This paper presents and tests a general model to predict emergent exchange patterns and power differences in reciprocal exchange networks when individual actors follow the norm of reciprocity. With an interesting qualification, the experimental results reported here support the power-dependence approach (Emerson 1972a, b): those who acquire the…
Descriptors: Interpersonal Communication, Power Structure, Prediction, Interpersonal Relationship
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Wanstrom, Linda – Multivariate Behavioral Research, 2009
Second-order latent growth curve models (S. C. Duncan & Duncan, 1996; McArdle, 1988) can be used to study group differences in change in latent constructs. We give exact formulas for the covariance matrix of the parameter estimates and an algebraic expression for the estimation of slope differences. Formulas for calculations of the required sample…
Descriptors: Sample Size, Effect Size, Mathematical Formulas, Computation
SenGupta, Saumitra – 1992
A way of identifying non-random patterns of effects on a group of individuals as a result of some intervention when a sample of participants is arrayed according to some indices of similarity is presented. The principle of proximal similarity and the concept of pattern matching provide the background for this effort. Major advantages are the…
Descriptors: Computer Simulation, Maps, Matrices, Multidimensional Scaling
Jarrell, Michele G. – 1991
A probability distribution was developed for the Andrews-Pregibon (AP) statistic. The statistic, developed by D. F. Andrews and D. Pregibon (1978), identifies multivariate outliers. It is a ratio of the determinant of the data matrix with an observation deleted to the determinant of the entire data matrix. Although the AP statistic has been used…
Descriptors: Computer Simulation, Error of Measurement, Matrices, Multivariate Analysis
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ten Berge, Jos M. F.; Kiers, Henk A. L. – Psychometrika, 1996
Some uniqueness properties are presented for the PARAFAC2 model for covariance matrices, focusing on uniqueness in the rank two case of PARAFAC2. PARAFAC2 is shown to be usually unique with four matrices, but not unique with three unless a certain additional assumption is introduced. (SLD)
Descriptors: Analysis of Covariance, Computer Simulation, Equations (Mathematics), Least Squares Statistics
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Snijders, Tom A. B. – Psychometrika, 1991
A complete enumeration method and a Monte Carlo method are presented to calculate the probability distribution of arbitrary statistics of adjacency matrices when these matrices have the uniform distribution conditional on given row and column sums, and possibly on a given set of structural zeros. (SLD)
Descriptors: Computer Simulation, Equations (Mathematics), Mathematical Models, Matrices
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Reddon, John R.; And Others – Journal of Educational Statistics, 1985
Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)
Descriptors: Computer Simulation, Correlation, Error of Measurement, Matrices
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Lautenschlager, Gary J.; And Others – Educational and Psychological Measurement, 1989
A method for estimating the first eigenvalue of random data correlation matrices is reported, and its precision is demonstrated via comparison to the method of S. J. Allen and R. Hubbard (1986). Data generated in Monte Carlo simulations with 10 sample sizes reaching up to 1,000 were used. (SLD)
Descriptors: Computer Simulation, Correlation, Equations (Mathematics), Estimation (Mathematics)
Kirisci, Levent; Hsu, Tse-Chi – 1993
Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…
Descriptors: Computer Simulation, Equations (Mathematics), Mathematical Models, Matrices
Kaplan, David – 1993
The impact of the use of data arising from balanced incomplete block (BIB) spiralled designs on the chi-square goodness-of-fit test in factor analysis is considered. Data from BIB designs posses a unique pattern of missing data that can be characterized as missing completely at random (MCAR). Standard approaches to factor analyzing such data rest…
Descriptors: Chi Square, Computer Simulation, Correlation, Factor Analysis
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Brown, R. L. – Educational and Psychological Measurement, 1989
Three correlation matrices (PEARSON, POLYCHORIC, and TOBIT) were used to obtain reliability estimates on ordered polytomous variable models. A Monte Carlo study with different levels of variable asymmetry and 400 sample correlation matrices demonstrated that the PEARSON matrix did not perform as well as did the other 2 matrices. (SLD)
Descriptors: Analysis of Covariance, Comparative Analysis, Computer Simulation, Correlation
Beasley, T. Mark; Leitner, Dennis W. – 1994
The use of stepwise regression has been criticized for both interpretive misunderstandings and statistical aberrations. A major statistical problem with stepwise regression and other procedures that involve multiple significance tests is the inflation of the Type I error rate. General approaches to control the family-wise error rate such as the…
Descriptors: Algorithms, Computer Simulation, Correlation, Error of Measurement
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Kloot, Willem A. van der; Herk, Hester van – Multivariate Behavioral Research, 1991
Two sets of real sorting data from 50 college students are used to compare results of multidimensional scaling of raw co-occurrence frequencies or dissimilarity measures (D) and profile distances (delta) to determine which yields a better representation of the underlying structure of 2 sets of stimuli. Slight differences are discussed. (SLD)
Descriptors: Classification, Cognitive Processes, College Students, Comparative Analysis
Rule, David L. – 1993
Several regression methods were examined within the framework of weighted structural regression (WSR), comparing their regression weight stability and score estimation accuracy in the presence of outlier contamination. The methods compared are: (1) ordinary least squares; (2) WSR ridge regression; (3) minimum risk regression; (4) minimum risk 2;…
Descriptors: Analysis of Covariance, Bayesian Statistics, Comparative Analysis, Computer Simulation