Obtaining representative samples from populations of emerging adults who do not attend college is challenging for researchers. This article introduces respondent-driven sampling (RDS), a method for obtaining representative samples of hard-to-reach but socially interconnected populations. RDS combines a prescribed method for chain referral with a…

This study builds on earlier work by DellaVecchio and Winston (2004) and McPherson (2008). They addressed the seven motivational gifts Paul wrote about in Romans 12:3-8 as a means for addressing job satisfaction and person-job fit among college professors. Using a snowball sampling method, 89 college professors completed the online survey…

This paper provides a guide to calculating statistical power for the complex multilevel designs that are used in most field studies in education research. For multilevel evaluation studies in the field of education, it is important to account for the impact of clustering on the standard errors of estimates of treatment effects. Using ideas from…

This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…

Cluster sampling results in response variable variation both among respondents (i.e., within-cluster or Level 1) and among clusters (i.e., between-cluster or Level 2). Properly modeling within- and between-cluster variation could be of substantive interest in numerous settings, but applied researchers typically test only within-cluster (i.e.,…

This study examines the work of US school principals from the perspective of their workday using a distributed perspective to frame the investigation. Using data on 38 school principals in one mid-sized urban school district in the US, it describes school principals' work practices, examining both the "focus" of that work and…

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…

It is a false, but common, belief that statistical significance testing evaluates result replicability. In truth, statistical significance testing reveals nothing about results replicability. Since science is based on replication of results, methods that assess replicability are important. This is particularly true when multivariate methods, which…

Theoretical hypotheses generated from data analysis of a single sample should not be advanced until the replicability issue is treated. At least one of three questions usually arises when evaluating the invariance of results obtained from a canonical correlation analysis (CCA): (1) "Will an effect occur in subsequent studies?"; (2)…

A method is presented for approximating the amount of bias in estimators with complex sampling distributions that are influenced by a variety of properties. The model is illustrated in the contexts of the bootstrap method and redundancy analysis. (SLD)

The Gibbs sampling algorithms presented by M. H. Seltzer (1993) are fully generalized to a broad range of settings in which vectors of random regression parameters in the hierarchical model are assumed multivariate normally or multivariate "t" distributed across groups. The use of a fully Bayesian approach is discussed. (SLD)

Multivariate methods are being used with increasing frequency in educational research because these methods control "experimentwise" error rate inflation, and because the methods best honor the nature of the reality to which the researcher wishes to generalize. This paper: explains the basic logic of canonical analysis; illustrates that…

L-scaling is introduced as a technique for determining the weights in weighted averages or scaled scores for T joint observations on K variables. The technique is so named because of its formal resemblance to the Leontief matrix of mathematical economics. L-scaling is compared to several widely-used procedures for data reduction, and the…

In the present study Monte Carlo methods were employed to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. Sampling error influences were investigated across variations in variable and sample (n) sizes, and across variations in average within-set correlation sizes and in…

Some years ago, B. Efron and his colleagues developed bootstrap resampling methods as a way of estimating the degree to which statistical results will replicate across variations in sample. A basic problem in the multivariate use of bootstrap procedures involves the requirement that the results across resamplings must be rotated to best fit in a…