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Beasley, William Howard; DeShea, Lise; Toothaker, Larry E.; Mendoza, Jorge L.; Bard, David E.; Rodgers, Joseph Lee – Psychological Methods, 2007
This article proposes 2 new approaches to test a nonzero population correlation ([rho]): the hypothesis-imposed univariate sampling bootstrap (HI) and the observed-imposed univariate sampling bootstrap (OI). The authors simulated correlated populations with various combinations of normal and skewed variates. With [alpha[subscript "set"]]=0.05, N…
Descriptors: Correlation, Sampling, Sample Size, Research Methodology
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Zou, Guang Yong – Psychological Methods, 2007
Confidence intervals are widely accepted as a preferred way to present study results. They encompass significance tests and provide an estimate of the magnitude of the effect. However, comparisons of correlations still rely heavily on significance testing. The persistence of this practice is caused primarily by the lack of simple yet accurate…
Descriptors: Intervals, Effect Size, Research Methodology, Correlation
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Graham, John W.; Taylor, Bonnie J.; Olchowski, Allison E.; Cumsille, Patricio E. – Psychological Methods, 2006
The authors describe 2 efficiency (planned missing data) designs for measurement: the 3-form design and the 2-method measurement design. The 3-form design, a kind of matrix sampling, allows researchers to leverage limited resources to collect data for 33% more survey questions than can be answered by any 1 respondent. Power tables for estimating…
Descriptors: Cost Effectiveness, Structural Equation Models, Psychological Studies, Data Collection
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Long, Jeffrey D. – Psychological Methods, 2005
Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q[superscript 2]…
Descriptors: Simulation, Social Science Research, Error of Measurement, Least Squares Statistics