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Hamaker, E. L.; Grasman, R. P. P. P. – Psychometrika, 2012
Many psychological processes are characterized by recurrent shifts between distinct regimes or states. Examples that are considered in this paper are the switches between different states associated with premenstrual syndrome, hourly fluctuations in affect during a major depressive episode, and shifts between a "hot hand" and a…
Descriptors: Psychological Patterns, Statistical Inference, Data, Simulation
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Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S. – Psychometrika, 2012
We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Reliability
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Yuan, Ke-Hai; Bentler, Peter M. – Psychometrika, 2004
Since data in social and behavioral sciences are often hierarchically organized, special statistical procedures for covariance structure models have been developed to reflect such hierarchical structures. Most of these developments are based on a multivariate normality distribution assumption, which may not be realistic for practical data. It is…
Descriptors: Statistical Analysis, Statistical Inference, Statistical Distributions, Multivariate Analysis
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Schroeder, Marsha L.; Hakstian, A. Ralph – Psychometrika, 1990
A 2-facet measurement model is identified, and its coefficient of generalizability (CG) is examined. Three other multifaceted measurement models and their CGs are identified. An empirical investigation of all four procedures is conducted using data from a study of the psychopathology of 71 prison inmates. (SLD)
Descriptors: Comparative Analysis, Equations (Mathematics), Generalizability Theory, Mathematical Models
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Frees, Edward W.; Kim, Jee-Seon – Psychometrika, 2006
Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling--model disturbances, random coefficients, and future response…
Descriptors: Prediction, School Effectiveness, Statistical Inference, Geometric Concepts
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Hakstian, A. Ralph; And Others – Psychometrika, 1989
Four measurement designs are presented for use with correlation coefficients corrected, in one variable, for attenuation due to unreliability (partially disattenuated). Associated asymptotic variance/covariance expressions are presented. Empirical simulation results illustrate the satisfactory Type I error control and statistical power of the…
Descriptors: Analysis of Covariance, Analysis of Variance, Correlation, Equations (Mathematics)
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Revuelta, Javier – Psychometrika, 2004
Two psychometric models are presented for evaluating the difficulty of the distractors in multiple-choice items. They are based on the criterion of rising distractor selection ratios, which facilitates interpretation of the subject and item parameters. Statistical inferential tools are developed in a Bayesian framework: modal a posteriori…
Descriptors: Multiple Choice Tests, Psychometrics, Models, Difficulty Level
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Arnold, Barry C.; And Others – Psychometrika, 1993
Inference is considered for the marginal distribution of "X" when ("X", "Y") has a truncated bivariate normal distribution. The "Y" variable is truncated, but only the "X" values are observed. A sample of 87 Otis test scores is shown to be well described by this model. (SLD)
Descriptors: Admission (School), Computer Simulation, Equations (Mathematics), Mathematical Models
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Hakstian, A. Ralph; And Others – Psychometrika, 1988
A model and computation procedure based on classical test score theory are presented for determination of a correlation coefficient corrected for attenuation due to unreliability. Delta and Monte Carlo method applications are discussed. A power analysis revealed no serious loss in efficiency resulting from correction for attentuation. (TJH)
Descriptors: Correlation, Equations (Mathematics), Hypothesis Testing, Mathematical Models