NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Preacher, Kristopher J.; Zyphur, Michael J.; Zhang, Zhen – Psychological Methods, 2010
Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover,…
Descriptors: Structural Equation Models, Hypothesis Testing, Statistical Analysis, Predictor Variables
Peer reviewed Peer reviewed
Direct linkDirect link
DeCoster, Jamie; Iselin, Anne-Marie R.; Gallucci, Marcello – Psychological Methods, 2009
Despite many articles reporting the problems of dichotomizing continuous measures, researchers still commonly use this practice. The authors' purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contacted 66 researchers who had published articles using…
Descriptors: Statistical Analysis, Classification, Monte Carlo Methods, Predictor Variables
Peer reviewed Peer reviewed
Direct linkDirect link
Collins, Linda M.; Dziak, John J.; Li, Runze – Psychological Methods, 2009
An investigator who plans to conduct an experiment with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy.…
Descriptors: Statistical Analysis, Behavioral Sciences, Social Sciences, Social Scientists
Peer reviewed Peer reviewed
Direct linkDirect link
Strobl, Carolin; Malley, James; Tutz, Gerhard – Psychological Methods, 2009
Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and…
Descriptors: Artificial Intelligence, Decision Making, Psychological Studies, Research Methodology
Peer reviewed Peer reviewed
Direct linkDirect link
Enders, Craig K.; Tofighi, Davood – Psychological Methods, 2007
Appropriately centering Level 1 predictors is vital to the interpretation of intercept and slope parameters in multilevel models (MLMs). The issue of centering has been discussed in the literature, but it is still widely misunderstood. The purpose of this article is to provide a detailed overview of grand mean centering and group mean centering in…
Descriptors: Predictor Variables, Item Response Theory, Statistical Analysis, Research
Peer reviewed Peer reviewed
Direct linkDirect link
Schafer, Joseph L.; Kang, Joseph – Psychological Methods, 2008
In a well-designed experiment, random assignment of participants to treatments makes causal inference straightforward. However, if participants are not randomized (as in observational study, quasi-experiment, or nonequivalent control-group designs), group comparisons may be biased by confounders that influence both the outcome and the alleged…
Descriptors: Research Methodology, Inferences, Psychological Studies, Simulation
Peer reviewed Peer reviewed
Direct linkDirect link
Klugkist, Irene; Laudy, Olav; Hoijtink, Herbert – Psychological Methods, 2005
Researchers often have one or more theories or expectations with respect to the outcome of their empirical research. When researchers talk about the expected relations between variables if a certain theory is correct, their statements are often in terms of one or more parameters expected to be larger or smaller than one or more other parameters.…
Descriptors: Researchers, Bayesian Statistics, Mathematical Concepts, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Serlin, Ronald C.; Harwell, Michael R. – Psychological Methods, 2004
It is well-known that for normally distributed errors parametric tests are optimal statistically, but perhaps less well-known is that when normality does not hold, nonparametric tests frequently possess greater statistical power than parametric tests, while controlling Type I error rate. However, the use of nonparametric procedures has been…
Descriptors: Multiple Regression Analysis, Monte Carlo Methods, Nonparametric Statistics, Error Patterns