A History of Public Education in the Town of Islip, New York

The purposes of this study were, 1. To develop a documented history of the founding of the town of Islip. 2. To trace the development of public education within the town and to parallel this development with state-wide developments in the field

The Disaggregation of Within-Person and Between-Person Effects in Longitudinal Models of Change

Longitudinal models are becoming increasingly prevalent in the behavioral sciences, with key advantages including increased power, more comprehensive measurement, and establishment of temporal precedence. One particularly salient strength offered by longitudinal data is the ability to disaggregate between-person and within-person effects in the regression of an outcome on a time-varying covariate. However, the ability to disaggregate these effects has not been fully capitalized upon in many social science research applications. Two likely reasons for this omission are the general lack of discussion of disaggregating effects in the substantive literature and the need to overcome several remaining analytic challenges that limit existing quantitative methods used to isolate these effects in practice. This review explores both substantive and quantitative issues related to the disaggregation of effects over time, with a particular emphasis placed on the multilevel model. Existing analytic methods are reviewed, a general approach to the problem is proposed, and both the existing and proposed methods are demonstrated using several artificial data sets. Potential limitations and directions for future research are discussed, and recommendations for the disaggregation of effects in practice are offered

An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.

Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not well-known. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions

Advantages of Integrative Data Analysis for Developmental Research

Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis is becoming an increasingly valuable tool. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item level data across multiple studies to make inferences possible both within and across studies and can be used to test questions not possible in individual contributing studies. Some of the potential benefits of IDA include the ability to study longer developmental periods, examine how the measurement of key constructs changes over time, increase subject heterogeneity, and improve statistical power and capability to study rare behaviors. Our goal in this paper is to provide a brief overview of the benefits and challenges of IDA in developmental research and to identify additional resources that provide more detailed discussions of this topic

Validity Concerns With Multiplying Ordinal Items Defined by Binned Counts: An Application to a Quantity-Frequency Measure of Alcohol Use

Social and behavioral scientists often measure constructs that are truly discrete counts by collapsing (or binning) the counts into a smaller number of ordinal responses. While prior quantitative research has identified a series of concerns with similar binning procedures, there has been a lack of study on the consequences of multiplying these ordinal items to create a desired index. This measurement strategy is incorporated in many research applications, but it is particularly salient in the study of substance use where the product of ordinal quantity (number of drinks) and frequency (number of days) items is used to create an index of total consumption. In the current study, we demonstrate both analytically and empirically that this multiplicative procedure can introduce serious threats to construct validity. These threats, in turn, directly impact the ability to accurately measure alcohol consumption

The Use of Latent Trajectory Models in Psychopathology Research.

Despite the recent surge in the development of powerful modeling strategies to test questions about individual differences in stability and change over time, these methods are not currently widely used in psychopathology research. In an attempt to further the dissemination of these new methods, the authors present a pedagogical introduction to the structural equation modeling based latent trajectory model, or LTM. They review several different types of LTMs, discuss matching an optimal LTM to a given question of interest, and highlight several issues that might be particularly salient for research in psychopathology. The authors augment each section with a review of published applications of these methods in psychopathology-related research to demonstrate the implementation and interpretation of LTMs in practice

Integrative data analysis: The simultaneous analysis of multiple data sets.

Both quantitative and methodological techniques exist that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from multiple studies, many advantages stem from the statistical analysis of the pooled data. The authors define integrative data analysis (IDA) as the analysis of multiple data sets that have been pooled into one. Although variants of IDA have been incorporated into other scientific disciplines, the use of these techniques are much less evident in psychology. In this paper the authors present an overview of IDA as it may be applied within the psychological sciences; a discussion of the relative advantages and disadvantages of IDA; a description of analytic strategies for analyzing pooled individual data; and offer recommendations for the use of IDA in practice

Twelve Frequently Asked Questions About Growth Curve Modeling

Longitudinal data analysis has long played a significant role in empirical research within the developmental sciences. The past decade has given rise to a host of new and exciting analytic methods for studying between-person differences in within-person change. These methods are broadly organized under the term growth curve models. The historical lines of development leading to current growth models span multiple disciplines within both the social and statistical sciences, and this in turn makes it challenging for developmental researchers to gain a broader understanding of the current state of this literature. To help address this challenge, the authors pose 12 questions that frequently arise in growth curve modeling, particularly in applications within developmental psychology. They provide concise and nontechnical responses to each question and make specific recommendations for further readings

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious and error-prone task. This article provides an overview of methods used to probe interaction effects and describes a unified collection of freely available online resources that researchers can use to obtain significance tests for simple slopes, compute regions of significance, and obtain confidence bands for simple slopes across the range of the moderator in the MLR, HLM, and LCA contexts. Plotting capabilities are also provided

The developmental psychopathology of alcohol use and alcohol disorders: Research achievements and future directions

The last 25 years have seen significant advances in our conceptualization of alcohol use and alcohol use disorders within a developmental framework, along with advances in our empirical understanding that have been potentiated by advances in quantitative methods. These include advances in understanding the heterogeneity of trajectories of alcohol outcomes; new insights about early childhood antecedents, and adolescence and emerging adulthood as important developmental periods for alcohol outcomes; a more nuanced understanding of the influences of developmental transitions, and their timing and contexts; a greater appreciation for the importance of considering multiple levels of analysis (including an increasing number of genetically informative studies); a continuing focus on studying multiple pathways underlying alcohol outcomes; and an increasing focus on studying the effects of alcohol exposure on future development. The current paper reviews these advances and suggests directions for future study