Resolving Dimensionality in a Child Assessment Tool: An Application of the Multilevel Bifactor Model.

Multidimensionality and hierarchical data structure are common in assessment data. These design features, if not accounted for, can threaten the validity of the results and inferences generated from factor analysis, a method frequently employed to assess test dimensionality. In this article, we describe and demonstrate the application of the multilevel bifactor model to address these features in examining test dimensionality. The tool for this exposition is the Child Observation Record Advantage 1.5 (COR-Adv1.5), a child assessment instrument widely used in Head Start programs. Previous studies on this assessment tool reported highly correlated factors and did not account for the nesting of children in classrooms. Results from this study show how the flexibility of the multilevel bifactor model, together with useful model-based statistics, can be harnessed to judge the dimensionality of a test instrument and inform the interpretability of the associated factor scores.

Keel bone differences in laying hens housed in enriched colony cages.

Keel bone damage may be painful to birds and affect their production. In order to better understand the frequency, position, and timepoint of keel bone damage that occur during production, the integrity of W-36 laying hen keel bones housed in enriched colony cages at 748.4 cm2 (116 in2) was evaluated. At four time points, 120 birds (10 per cage; three cages per each of four rooms) had keel bones evaluated. Each hen was placed in a motion limiting restraint, scanned using computed tomography (CT), fitted in vests containing tri-axial accelerometers, and placed back in their cages for 21 d. After 21 d, the hens were rescanned and returned to their cages. This process was repeated after 133 d. The CT scans were imported into Mimics analysis software (Materialise, Plymouth, MI, USA); 3D models were made of each keel bone at each time point and exported to 3-matic analysis software (Materialise, Plymouth, MI, USA). Each laying hen's keel bone model was superimposed onto scans from multiple time points resulting in four bone pairings representative of each 21-d period, the 133-d period, and the entire duration of the project. Next, the proximal portion of each bone pairing was edited to normalize bone shape according to a strict protocol. Additionally, each pairing was divided into three portions: distal aspect (3 cm), proximal aspect (2 cm), and middle portion (remaining). Whole bone pairing and each bone portion was analyzed using the Part Comparison tool in 3-matic. Raw data were compiled into three datasets and analyzed in SAS 9.3 using the GLIMMIX procedure using a three-level random intercept model. The model controlled for time, part, part(time), and system with random intercepts of bird(cage) and cage. Overall, results revealed that the greatest morphological changes occurred during the first 21-d period with regards to time (P = 0.03) and in the distal aspect of the keel with regards to part (P < 0.0001).

Comparing Between- and Within-Group Variances in a Two-Level Study: A Latent Variable Modeling Approach to Evaluating Their Relationship.

This note is concerned with examining the relationship between within-group and between-group variances in two-level nested designs. A latent variable modeling approach is outlined that permits point and interval estimation of their ratio and allows their comparison in a multilevel study. The procedure can also be used to test various hypotheses about the discrepancy between these two variances and assist with their relationship interpretability in empirical investigations. The method can also be utilized as an addendum to point and interval estimation of the popular intraclass correlation coefficient in hierarchical designs. The discussed approach is illustrated with a numerical example.