Preface

Bock, Rosilin

doi:10.1016/b978-0-12-108840-8.50004-4

Cited by 11 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several statistical methodologies have been developed to handle the problem of comparing group percentages or means when observations taken within groups are “clustered,” as are observations taken from related individuals (see Bock 1989). For example, an ANOVA method for a nested design would be best for comparing the mean of relatives of probands with the mean of relatives of controls.…”

Section: Further Methodological Considerationsmentioning

confidence: 99%

Eye Tracking Dysfunction and Schizophrenia: A Critical Perspective

Levy

Holzman

Matthysse

et al. 1993

Schizophrenia Bulletin

276

196

View full text Add to dashboard Cite

Eye tracking dysfunction (ETD) has been found in large numbers of schizophrenia patients and their first-degree relatives. Because of the many replications of the central findings, ETD has been proposed as a useful way of expanding the schizophrenia phenotype in genetic studies. We critically review the literature on ETD with respect to issues of measurement and the search for quantitative indices of ETD; syndrome and familial specificity of ETD for schizophrenia; statistical, interpretive, and methodological considerations in the use of mixture analysis; the association of ETD with clinically and psychometrically defined schizotypy; and the questions of trait stability and medication effects.

show abstract

Section: Further Methodological Considerationsmentioning

confidence: 99%

Eye Tracking Dysfunction and Schizophrenia: A Critical Perspective

Levy

Holzman

Matthysse

et al. 1993

Schizophrenia Bulletin

276

196

View full text Add to dashboard Cite

show abstract

“…Under multilevel data structure, ρ can be interpreted as the correlation between individuals within the same cluster (Bock, 1989). The estimated correlation is as follows (e.g., Crocker & Algina, 1986; Murphy & Davidshofer, 1988; Zimmerman & Williams, 1977):…”

mentioning

confidence: 99%

Complex Versus Simple Modeling for DIF Detection

Jin

Myers

Ahn

2013

Educational and Psychological Measurement

View full text Add to dashboard Cite

Previous research has demonstrated that differential item functioning (DIF) methods that do not account for multilevel data structure could result in too frequent rejection of the null hypothesis (i.e., no DIF) when the intraclass correlation coefficient (ρ) of the studied item was the same as the ρ of the total score. The current study extended previous research by comparing the performance of DIF methods when ρ of the studied item was less than ρ of the total score, a condition that may be observed with considerable frequency in practice. The performance of two simple and frequently used DIF methods that do not account for multilevel data structure, the Mantel–Haenszel test (MH) and logistic regression (LR), was compared with the performance of a complex and less frequently used DIF method that does account for multilevel data structure, hierarchical logistic regression (HLR). Simulation indicated that HLR and LR performed equivalently in terms of significance tests under most conditions, and MH was conservative across most of the conditions. Effect size estimate of HLR was equally accurate and consistent as effect size estimates of LR and MH under the Rasch model and was more accurate and consistent than LR and MH effect size estimates under the two-parameter item response theory model. The results of the current study provide evidence to help researchers further understand the comparative performance between complex and simple modeling for DIF detection under multilevel data structure.

show abstract

“…Here, keywords include slopes-as-outcomes, multilevel modeling, and hierarchical linear modeling (HLM). Key references include Cronbach (1976), Burstein (1980), Goldstein (1987, 1995), Bock (1989), Bryk and Raudenbush (1992), and Longford (1993). Key software products include MLn, HLM, and VARCL.…”

mentioning

confidence: 99%

General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation.

Muthén

Curran²

1997

Psychological Methods

642

585

View full text Add to dashboard Cite

The generality of latent variable modeling of individual differences in development over time is demonstrated with a particular emphasis on randomized intervention studies. First, a brief overview is given of biostatistical and psychometric approaches to repeated measures analysis. Second, the generality of the psychometric approach is indicated by some nonstandard models. Third, a multiple-population analysis approach is proposed for the estimation of treatment effects. The approach clearly describes the treatment effect as development that differs from normative, control-group development. This framework allows for interactions between treatment and initial status in their effects on development. Finally, an approach for the estimation of power to detect treatment effects in this framework is demonstrated.Illustrations of power calculations are carried out with artificial data, varying the sample sizes, number of timepoints, and treatment effect sizes. Real data are used to illustrate analysis strategies and power calculations. Further modeling extensions are discussed.

show abstract

Preface

Cited by 11 publications

References 0 publications

Eye Tracking Dysfunction and Schizophrenia: A Critical Perspective

Eye Tracking Dysfunction and Schizophrenia: A Critical Perspective

Complex Versus Simple Modeling for DIF Detection

General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation.

Contact Info

Product

Resources

About