Secure Base Provision: A New Approach to Examining Links Between Maternal Caregiving and Infant Attachment

This observational study addressed a critical gap in the understanding of the precursors of infant attachment by examining whether a new conceptualization of maternal caregiving behavior, secure base provision (SBP), explained variance in attachment above and beyond variance explained by sensitivity. Participants included 83 low-socioeconomic status (SES), 4.5-month-old infants (56% male) and their mothers. Infant–mother dyads completed laboratory tasks at 4.5 months and three 30-min home visits between 7 and 9 months, then returned to the laboratory at 12 months for an attachment assessment. Maternal sensitivity did not significantly predict infant attachment security. SBP significantly predicted infant attachment, over and above sensitivity, with an effect size eight times larger than that of sensitivity in meta-analytic findings for low-SES families.     

HyperCard for the Design,Development, and Delivery of Instruction

The innovative software product HyperCard™ introduced in August, 1987, and today distributed free of charge with Apple Computer Incorporated's Macintosh line of computers, has sparked a flurry of interest among computer users. HyperCard represents advanced technology that, in a short time, has been put in the hands of hundreds of thousands of users, including a significant number of instructional designers and performance technologists. This article has three sections that introduce instructional designers and performance technologists to HyperCard. The first section describes just what HyperCard is and summarizes how it works. The second section presents the theoretical background of the program, from the perspectives of both cognitive psychologists and futuristic thinkers in the computing world. Finally, the third section describes three uses of HyperCard: (a) as a database management program, (b) as a vehicle for implementing hypermedia applications, and (c) as an authoring tool. For each of these three functions, the advantages and disadvantages of HyperCard for instructional designers and performance technologists are explored.     

Kindergarten screening: A survey of current practice

This study investigated current practices for screening new entrants to schools. A mail survey was conducted of 755 public‐ and private‐school districts in New York State to ascertain the procedures and instruments used to conduct kindergarten screening. Fifty‐one percent (385) of the districts responded. Thirty percent of the districts reported that they used locally developed screening instruments. The most widely used standardized instruments were the Developmental Indicators for the Assessment of Learning (DIAL‐R) (Mardell‐Czudnowski & Goldenberg, 1990), and the Brigance K & 1 Screen (Brigance, 1992). The great majority of respondents (95%) reported that screening is conducted on an individual basis, and in 45% of the districts, professionals spend more than 30 minutes in screening procedures with each child. On average, 3.58 different educational professionals in each district participate in screening procedures. About half of the districts reported that children identified as “unready” on the basis of screening procedures were referred for further evaluation; 19% of the districts advised parents to delay school entry for an additional year. Most districts (69%) reported that they were satisfied or very satisfied with current district practice. Implications for practitioners are discussed. © 2000 John Wiley & Sons, Inc.     

Digital Module 06: Bayesian Psychometrics—Posterior Predictive Model Checking https://ncme.elevate.commpartners.com

Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model‐data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model‐data fit models is critical. In this instructional module, Allison Ames and Aaron Myers provide an overview of Posterior Predictive Model Checking (PPMC), the most common Bayesian model‐data fit approach. Specifically, they review the conceptual foundation of Bayesian inference as well as PPMC and walk through the computational steps of PPMC using real‐life data examples from simple linear regression and item response theory analysis. They provide guidance for how to interpret PPMC results and discuss how to implement PPMC for other model(s) and data. The digital module contains sample data, SAS code, diagnostic quiz questions, data‐based activities, curated resources, and a glossary.