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Sang-June Park; Youjae Yi – Journal of Educational and Behavioral Statistics, 2024
Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…
Descriptors: Interaction, Predictor Variables, Causal Models, Mathematical Models
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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
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Bavin, Edith L.; Grayden, David B.; Scott, Kim; Stefanakis, Toni – Language and Speech, 2010
Infants' auditory processing abilities have been shown to predict subsequent language development. In addition, poor auditory processing skills have been shown for some individuals with specific language impairment. Methods used in infant studies are not appropriate for use with young children, and neither are methods typically used to test…
Descriptors: Intervals, Speech Impairments, Testing, Young Children
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Williams, Jason; MacKinnon, David P. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
Recent advances in testing mediation have found that certain resampling methods and tests based on the mathematical distribution of 2 normal random variables substantially outperform the traditional "z" test. However, these studies have primarily focused only on models with a single mediator and 2 component paths. To address this limitation, a…
Descriptors: Intervals, Testing, Predictor Variables, Effect Size
Watt, James H., Jr. – 1979
A relatively simple procedure for modeling periodic components in time series data is presented in this paper, along with an example of the procedure's use with communication data. Similar to multiple regression analysis, the described procedure has four steps that are based on information about periodic waves and their components, how to create…
Descriptors: Communication Research, Componential Analysis, Data Analysis, Evaluation Methods