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Barchard, Kimberly A. – Psychological Methods, 2012
This article introduces new statistics for evaluating score consistency. Psychologists usually use correlations to measure the degree of linear relationship between 2 sets of scores, ignoring differences in means and standard deviations. In medicine, biology, chemistry, and physics, a more stringent criterion is often used: the extent to which…
Descriptors: Psychologists, Error of Measurement, Correlation, Reliability
Ludtke, Oliver; Marsh, Herbert W.; Robitzsch, Alexander; Trautwein, Ulrich – Psychological Methods, 2011
In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in the social sciences is that there is no error-free measurement of constructs. In the present article, 2 types of error occurring in multilevel data…
Descriptors: Simulation, Educational Psychology, Social Sciences, Measurement
Ludtke, Oliver; Marsh, Herbert W.; Robitzsch, Alexander; Trautwein, Ulrich; Asparouhov, Tihomir; Muthen, Bengt – Psychological Methods, 2008
In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach,…
Descriptors: Statistical Analysis, Sampling, Context Effect, Simulation
Rae, Gordon – Psychological Methods, 2007
The relationship between stratified alpha (alpha-sub(s)) and the reliability of a test composed of interrelated nonhomogeneous items is examined. It is mathematically demonstrated that when there is congeneric equivalence within the strata or subtests, the difference between the coefficients is a function of the variances of the loadings within…
Descriptors: Test Reliability, Test Items, Computation, Error of Measurement
Woods, Carol M. – Psychological Methods, 2007
This research focused on confidence intervals (CIs) for 10 measures of monotonic association between ordinal variables. Standard errors (SEs) were also reviewed because more than 1 formula was available per index. For 5 indices, an element of the formula used to compute an SE is given that is apparently new. CIs computed with different SEs were…
Descriptors: Intervals, Computation, Measurement Techniques, Error of Measurement
Eid, Michael; Nussbeck, Fridtjof W.; Geiser, Christian; Cole, David A.; Gollwitzer, Mario; Lischetzke, Tanja – Psychological Methods, 2008
The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods…
Descriptors: Structural Equation Models, Multitrait Multimethod Techniques, Statistical Analysis, Error of Measurement
Wetcher-Hendricks, Debra – Psychological Methods, 2006
With respect to the often-present covariance between error terms of correlated variables, D. W. Zimmerman and R. H. Williams's (1977) adjusted correction for attenuation estimates the strength of the pairwise correlation between true scores without assuming independence of error scores. This article focuses on the derivation and analysis of…
Descriptors: Correlation, Scores, Error Correction, Error of Measurement
Hsu, Louis M. – Psychological Methods, 2005
One version of r-sub(equivalent), calculated from Fisher's exact test p values and recommended for small samples, is considered "a more realistic... [and] a more accurate estimate of the population correlation than... the sample correlation, r-sub(sample)" (R. Rosenthal & D. B. Rubin, 2003, p. 494). Small sample properties of r-sub(sample) and of…
Descriptors: Correlation, Meta Analysis, Error of Measurement, Effect Size
Hertzog, Christopher; Lindenberger, Ulman; Ghisletta, Paolo; Oertzen, Timo von – Psychological Methods, 2006
We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris…
Descriptors: Multivariate Analysis, Models, Sample Size, Reliability