Showing papers by "John J. McArdle published in 2017"
27 May 2017
TL;DR: In this paper, the importance of improving leadership interactions with their employees to increase workplace inclusion was highlighted, and favorable perceptions of leader-member exchange were associated with increased feelings of inclusion 6 and 12 months later.
Abstract: With increased workforce diversity, human service organizations are recognizing the need to create inclusive workplaces; yet little is known about how leaders can enhance workplace inclusion. We collected data at three time points in 6-month intervals from a public child welfare organization (n = 363). Using latent change score models, we analyzed whether leader-member exchange influenced how inclusion changed over time. Results indicate that favorable perceptions of leader-member exchange are associated with increased feelings of inclusion 6 and 12 months later. Findings highlight the importance of improving leadership interactions with their employees to increase workplace inclusion.
45 citations
TL;DR: This first U.S. population-based GWAS study conducted on both age-related immediate and delayed verbal memory found that TOMM40 had effects independent of APOE e4 on both phenotypes, andConditional analyses indicate GWAS signals on rDR level were driven by APOE, whereas signals on IR change weredriven by TOMM 40.
Abstract: Verbal memory is typically studied using immediate recall (IR) and delayed recall (DR) scores, although DR is dependent on IR capability. Separating these components may be useful for deciphering the genetic variation in age-related memory abilities. This study was conducted to (a) construct individual trajectories in IR and independent aspects of delayed recall, or residualized-DR (rDR), across older adulthood; and (b) identify genetic markers that contribute to four estimated phenotypes: IR and rDR levels and changes after age 60. A cognitively intact sample (N = 20,650 with 125,164 observations) was drawn from the U.S. Health and Retirement Study, a nationally representative study of adults aged 50 and older. Mixed effects regression models were constructed using repeated measures from data collected every two years (1996-2012) to estimate level at age 60 and change in memory post-60 in IR and rDR. Genome-wide association scans (GWAS) were conducted in the genotypic subsample (N = 7,486) using ~1.2 million single nucleotide polymorphisms (SNPs). One SNP (rs2075650) in TOMM40 associated with rDR level at the genome-wide level (p = 5.0x10-08), an effect that replicated in an independent sample from the English Longitudinal Study on Ageing (N = 6,898 with 41,328 observations). Meta-analysis of rDR level confirmed the association (p = 5.0x10-11) and identified two others in TOMM40 (rs71352238 p = 1.0x10-10; rs157582 p = 7.0x10-09), and one in APOE (rs769449 p = 3.1 x10-12). Meta-analysis of IR change identified associations with three of the same SNPs in TOMM40 (rs157582 p = 8.3x10-10; rs71352238 p = 1.9x10-09) and APOE (rs769449 p = 2.2x10-08). Conditional analyses indicate GWAS signals on rDR level were driven by APOE, whereas signals on IR change were driven by TOMM40. Additionally, we found that TOMM40 had effects independent of APOE e4 on both phenotypes. Findings from this first U.S. population-based GWAS study conducted on both age-related immediate and delayed verbal memory merit continued examination in other samples and additional measures of verbal memory.
26 citations
TL;DR: This work considers the use of structural equation model trees as an alternative framework that does not assume the classes are latent and uses observed covariates to derive their structure and its respective strengths and limitations for creating homogeneous groups.
Abstract: Although finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation m...
21 citations
TL;DR: Simulation methods are used to investigate the performance of a model-based exploratory data mining technique—structural equation model trees—as a tool for detecting population heterogeneity and show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes.
Abstract: When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and manipulate the precision of the information provided by a covariate about the true latent profile as well as other factors, including sample size, under the possible influences of model misspecifications. Simulation results show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes. This can be attributed to the lower statistical power in identifying classes, resulting from smaller differences of parameters prescribed ...
17 citations
29 Sep 2017
TL;DR: In this paper, the authors provide growth mixture models into a nonlinear framework and examine patterns of cognitive development across the lifespan, including subpopulation of participants with a differential decline in late adulthood or subpopulations with a stunt in growth during adolescence.
Abstract: This chapter provides growth mixture models into a nonlinear framework and examines patterns of cognitive development across the lifespan. It presents the work on nonlinear growth mixture models to include models based on structured curves, growth models based on latent difference scores and related multivariate models. The chapter outlines the latent growth modeling with consideration for nonlinear, multiple group, and multivariate growth models, a short introduction to growth mixture modeling, and an application of nonlinear growth mixture models to lifespan cognitive development data. In the data file the measurements are grouped into common units of time/age. The latent difference score growth curves can model a series of nonlinear shapes and allow for the evaluation of time-dependent influences on the change in the variable of interest. Some of the possibilities in cognitive aging are subpopulations of participants with a differential decline in late adulthood or subpopulations with a stunt in growth during adolescence.
3 citations
TL;DR: This simulation study investigated two families of classification and regression trees (CART) and random forests approaches for addressing missing data in small samples and found inverse probability weights generated from the true missing data selection model served as a benchmark for other weighting methods.
Abstract: This simulation study investigated two families of classification and regression trees (CART) and random forests approaches for addressing missing data in small samples. The first approach uses CAR...