The Flynn effect and cognitive decline among americans aged 65 years and older.

To contribute to our understanding of cohort differences and the Flynn effect in the cognitive decline among older Americans, this study aims to compare rates of cognitive decline between two birth cohorts within a study of older Americans and to examine the importance of medical and demographic confounders. Analyses used data from the National Health and Aging Trends Study (2011-2019), which recruited older Americans in 2011 and again in 2015 who were then followed for 5 years. We employed mixed-effect models to examine the linear and quadratic main and interaction effects of year of birth while adjusting for covariates such as annual round, sex/gender, education, race/ethnicity, heart disease, hypertension, diabetes, test unfamiliarity, and survey design. We analyzed data from 11,167 participants: 7,325 from 2011 to 2015 and 3,842 from 2015 to 2019. The cohort recruited in 2015 was born, on average, 5.33 years later than that recruited in 2011 and had higher functioning than the one recruited in 2011 across all observed cognitive domains that persisted after adjusting for covariates. In multivariable-adjusted analyses, a 1-year increase in year of birth was associated with increased episodic memory (ß = 0.045, SE = 0.001, p < .001), orientation (ß = 0.034, SE = 0.001, p < .001), and executive function (ß = 0.036, SE = 0.001, p < .001). Participants born 1 year later had slower rates of decline in episodic memory (ß = 0.004, SE = 0.000, p < .001), orientation (ß = 0.003, SE = 0.000, p < .001), and executive function (ß = 0.001, SE = 0.000, p = .002). Additionally, sex/gender modified this relationship for episodic memory (-0.007, SE = 0.002, p < .001), orientation (-0.005, SE = 0.002, p = .008), and executive function (-0.008, SE = 0.002, p < .001). These results demonstrate the persistence of the Flynn effect in old age across cognitive domains and identified a deceleration in the rate of cognitive decline across cognitive domains. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Social Contagion and General Diffusion Models of Adolescent Religious Transitions: A Tutorial, and EMOSA Applications.

Epidemic Models of the Onset of Social Activities (EMOSA) describe behaviors that spread through social networks. Two social influence methods are represented, social contagion (one-to-one spread) and general diffusion (spread through cultural channels). Past models explain problem behaviors-smoking, drinking, sexuality, and delinquency. We provide review, and a tutorial (including examples). Following, we present new EMOSA models explaining changes in adolescent and young adult religious participation. We fit the model to 10 years of data from the 1997 U.S. National Longitudinal Survey of Youth. Innovations include a three-stage bi-directional model, Bayesian Markov Chain Monte Carlo (MCMC) estimation, graphical innovations, and empirical validation. General diffusion dominated rapid reduction in church attendance during adolescence; both diffusion and social contagion explained church attendance stability in early adulthood.

Home Improvement: Evaluating Secular Changes in NLSY HOME-Cognitive Stimulation and Emotional Support Scores.

This study evaluated changes over time in the quality of children's home environment, using the Home Observation Measurement of the Environment (HOME). Longitudinal increases in HOME scores were predicted by both theory and past empirical results. Analysis of the National Longitudinal Survey of Youth Children data (N = 5715, aged 0-14) suggested that HOME scores have been increasing, and that the increase is a family-level phenomenon. The data were a sample of children born to mothers who were approximately representative of the United States in 1979. An increase in HOME scores occurred primarily for the three age categories younger than ten. Effect sizes were of approximately the same magnitude as the Flynn effect for intelligence. These results have implications for policy and future research regarding the home environment.

Understanding the exploratory/confirmatory data analysis continuum: Moving beyond the "replication crisis".

In light of the "replication crisis," some advocate for stricter standards and greater transparency in research methods. These efforts push toward a data analysis approach called "confirmatory data analysis" (CDA; see Wagenmakers et al., 2012). However, some (e.g., Baumeister, 2016; Goldin-Meadow, 2016) suggest that emphasizing CDA may restrict creativity and discovery. These scholars argued (sometimes inadvertently) for greater freedom to pursue "exploratory data analysis" (EDA; see Tukey, 1977). Ironically and unfortunately, many who push against stricter CDA standards do not realize EDA exists, or misunderstand the philosophy and proper tools for exploration. In this article, the meaning, tools, philosophy, and ethics associated with EDA, CDA, and a relatively unknown but important approach called "rough CDA" are clarified. Important distinctions are developed between EDA/rough CDA/CDA and other (some problematic) analysis activities including p-hacking, HARKing, and data mining, which are situated in a (graphical) framework that clarifies relationships and ethical boundaries with each. In short, the proper data analytic approach depends on (a) intentions and (b) transparency. Most psychological research is not at a maturity level to justify CDA; researchers have historically used tools mismatched to their research agenda. In the conclusion, recommendations are presented about how these typologies can be integrated into graduate training programs and how a cumulative research program can help psychology move beyond the replication crisis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Tribute to the Mind, Methodology and Mentoring of Wayne Velicer.

Wayne Velicer is remembered for a mind where mathematical concepts and calculations intrigued him, behavioral science beckoned him, and people fascinated him. Born in Green Bay, Wisconsin on March 4, 1944, he was raised on a farm, although early influences extended far beyond that beginning. His Mathematics BS and Psychology minor at Wisconsin State University in Oshkosh, and his PhD in Quantitative Psychology from Purdue led him to a fruitful and far-reaching career. He was honored several times as a high-impact author, was a renowned scholar in quantitative and health psychology, and had more than 300 scholarly publications and 54,000+ citations of his work, advancing the arenas of quantitative methodology and behavioral health. In his methodological work, Velicer sought out ways to measure, synthesize, categorize, and assess people and constructs across behaviors and time, largely through principal components analysis, time series, and cluster analysis. Further, he and several colleagues developed a method called Testing Theory-based Quantitative Predictions, successfully applied to predicting outcomes and effect sizes in smoking cessation, diet behavior, and sun protection, with the potential for wider applications. With $60,000,000 in external funding, Velicer also helped engage a large cadre of students and other colleagues to study methodological models for a myriad of health behaviors in a widely applied Transtheoretical Model of Change. Unwittingly, he has engendered indelible memories and gratitude to all who crossed his path. Although Wayne Velicer left this world on October 15, 2017 after battling an aggressive cancer, he is still very present among us.

Cohort and Period Effects as Explanations for Declining Dementia Trends and Cognitive Aging.

Studies have reported that the age-adjusted incidence of cognitive impairment and dementia may have decreased over the past two decades. Aging is the predominant risk factor for Alzheimer's disease and related dementias and for neurocognitive decline. However, aging cannot explain changes in overall age-adjusted incidence of dementia. The objective of this position paper was to describe the potential for cohort and period effects in cognitive decline and incidence of dementia. Cohort effects have long been reported in demographic literature, but starting in the early 1980s, researchers began reporting cohort trends in cognitive function. At the same time, period effects have emerged in economic factors and stressors in early and midlife that may result in reduced cognitive dysfunction. Recognizing that aging individuals today were once children and adolescents, and that research has clearly noted that childhood cognitive performance is a primary determinant of old-age cognitive performance, this is the first study that proposes the need to connect known cohort effects in childhood cognition with differences in late-life functioning.

The Peer Victimization in College Survey: Construction and validation.

Colleges and universities are increasingly concerned about respect for diversity and tolerance of individual differences on their campuses. Nevertheless, no comprehensive measure of peer victimization has been developed and validated for use with college student populations. The Peer Victimization in College Survey (PVIC) is the first such measure. Study 1 (N = 733) reports how PVIC items were empirically derived to ensure construct coverage. Study 2 (N = 100) reports how intuitive PVIC subscales were established to distinguish between subtypes of college peer victimization. Study 3 (N = 520) provides evidence of convergent, discriminant, and construct validity for the PVIC, including its relations to risk factors and to outcomes such as depressive symptoms, anxiety, stress, and college sense of belonging. Study 4 (N = 633) validates several PVIC scaling methods and provides evidence of incremental validity of the measure over current (unvalidated) measures. The PVIC can assess subtypes of peer victimization on college campuses, evaluate the effectiveness of campus intervention efforts, and test hypotheses about the causes and effects of peer victimization. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A Simulation Study of Bootstrap Approaches to Estimate Confidence Intervals in DeFries-Fulker Regression Models (with Application to the Heritability of BMI Changes in the NLSY).

The univariate bootstrap is a relatively recently developed version of the bootstrap (Lee and Rodgers in Psychol Methods 3(1): 91, 1998). DeFries-Fulker (DF) analysis is a regression model used to estimate parameters in behavioral genetic models (DeFries and Fulker in Behav Genet 15(5): 467-473, 1985). It is appealing for its simplicity; however, it violates certain regression assumptions such as homogeneity of variance and independence of errors that make calculation of standard errors and confidence intervals problematic. Methods have been developed to account for these issues (Kohler and Rodgers in Behav Genet 31(2): 179-191, 2001), however the univariate bootstrap represents a unique means of doing so that is presaged by suggestions from previous DF research (e.g., Cherny et al. in Behav Genet 22(2): 153-162, 1992). In the present study we use simulations to examine the performance of the univariate bootstrap in the context of DF analysis. We compare a number of possible bootstrap schemes as well as more traditional confidence interval methods. We follow up with an empirical demonstration, applying results of the simulation to models estimated to investigate changes in body mass index in adults from the National Longitudinal Survey of Youth 1979 data.

The impact of having children on domain-specific life satisfaction: A quasi-experimental longitudinal investigation using the Socio-Economic Panel (SOEP) data.

Longitudinal studies have documented improvements in parents' life satisfaction due to childbearing, followed by postpartum adaptation back to baseline. However, the details underlying this process remain largely unexplored. Based on past literature, set-point theory, and results from an exploratory sample, we investigated empirically how first childbirth affected satisfaction with specific domains of life. In a preregistered study, we compared parents with matched childless respondents in their trajectories of life satisfaction, and also satisfaction with family life, health, sleep, work, housework, leisure, dwelling, household income, and personal income. First-time parents and childless respondents were matched in a procedure combining exact and propensity score matching. Using the population-representative German SOEP data (N = 3,370), longitudinal multilevel models revealed heterogeneous effects of childbirth on different domains of satisfaction: Both mothers' and fathers' satisfaction with family life increased temporarily in a similar fashion to life satisfaction before going back to baseline within five years after childbirth. However, only mothers experienced drastic losses to satisfaction with sleep and satisfaction with personal income. For the remaining domains, parents' satisfaction largely resembled that of the matched childless respondents. These divergent domain trajectories underscore the need for multivariate analyses in life satisfaction research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs.

In 1918, Fisher suggested that his research team had consistently found inflated cousin correlations. He also commented that because a cousin sample with minimal selection bias was not available the cause of the inflation could not be addressed, leaving this inflation as a challenge still to be solved. In the National Longitudinal Survey of Youth (the NLSY79, the NLSY97, and the NLSY-Children/Young Adult datasets), there are thousands of available cousin pairs. Those in the NLSYC/YA are obtained approximately without selection. In this paper, we address Fisher's challenge using these data. Further, we also evaluate the possibility of fitting ACE models using only cousin pairs, including full cousins, half-cousins, and quarter-cousins. To have any chance at success in such a restricted kinship domain requires an available and highly-reliable phenotype; we use adult height in our analysis. Results provide a possible answer to Fisher's challenge, and demonstrate the potential for using cousin pairs in a stand-alone analysis (as well as in combination with other biometrical designs).

Decomposing the causes of the socioeconomic status-health gradient with biometrical modeling.

The consistent relationship between socioeconomic status (SES) and health has been widely covered in the media and scientific journals, which typically argue that physical-health inequalities are caused by material disadvantage directly or indirectly (e.g., chronic environmental-stress, health care resources, etc.). Such explanations do not explain the finely stratified health differences across the entire range of SES. Recent theories have helped address such limitations, but implicate multiple different explanatory pathways. For example, differential epidemiology articles have argued that individual differences are the "fundamental cause" of the gradient (Gottfredson, 2004). Alternatively, variants of allostatic load theory (McEwen & Stellar, 1993), such as the Risky Families model (Repetti, Taylor, & Seeman, 2002) implicate the early home-environment. These theory-driven pathways align with interpretations associated with biometrical models; yet, little research has applied biometrical modeling to understanding the sources of the gradient. Our study presents several innovations and new research findings. First, we use kinship information from a large national family dataset, the NLSY79, whose respondents are approximately representative of United States adolescents in 1979. Second, we present the first biometrical analysis of the relationships between SES and health that uses an overall SES measure. Third, we separate physical and mental health, using excellent measurement of each construct. Fourth, we use a bivariate biometrical model to study overlap between health and SES. Results suggest divergent findings for physical and mental health. Biometrical models indicate a primarily genetic etiology for the link between SES and physical health, and a primarily environmental etiology for the link between SES and mental health. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Double Decomposition of Level-1 Variables in Multilevel Models: An Analysis of the Flynn Effect in the NSLY Data.

This paper introduces an extension of cluster mean centering (also called group mean centering) for multilevel models, which we call "double decomposition (DD)." This centering method separates between-level variance, as in cluster mean centering, but also decomposes within-level variance of the same variable. This process retains the benefits of cluster mean centering but allows for context variables derived from lower level variables, other than the cluster mean, to be incorporated into the model. A brief simulation study is presented, demonstrating the potential advantage (or even necessity) for DD in certain circumstances. Several applications to multilevel analysis are discussed. Finally, an empirical demonstration examining the Flynn effect (Flynn, 1987 ), our motivating example, is presented. The use of DD in the analysis provides a novel method to narrow the field of plausible causal hypotheses regarding the Flynn effect, in line with suggestions by a number of researchers (Mingroni, 2014 ; Rodgers, 2015 ).

Intelligence, income, and education as potential influences on a child's home environment: A (maternal) sibling-comparison design.

The quality of the home environment, as a predictor, is related to health, education, and emotion outcomes. However, factors influencing the quality of the home environment, as an outcome, have been understudied-particularly how children construct their own environments. Further, most previous research on family processes and outcomes has implemented between-family designs, which limit claims of causality. The present study uses kinship data from the National Longitudinal Survey of Youth to construct a maternal sibling-comparison design to investigate how maternal and child traits predict the quality of home environment. Using a standard between-family analysis, we first replicate previous research showing a relationship between maternal intelligence and the quality of the home environment. Then, we reevaluate the link between maternal intelligence and the home environment using differences between maternal sisters on several characteristics to explain differences between home environments for their children. Following, we evaluate whether child intelligence differences are related to home environment differences in the presence of maternal characteristics. Results are compared with those from the between-family analysis. Past causal interpretations are challenged by our findings, and the role of child intelligence in the construction of the home environment emerges as a critical contributor that increases in importance with development. (PsycINFO Database Record

The NLSY Kinship Links: Using the NLSY79 and NLSY-Children Data to Conduct Genetically-Informed and Family-Oriented Research.

The National Longitudinal Survey of Youth datasets (NLSY79; NLSY-Children/Young Adults; NLSY97) have extensive family pedigree information contained within them. These data sources are based on probability sampling, a longitudinal design, and a cross-generational and within-family data structure, with hundreds of phenotypes relevant to behavior genetic (BG) researchers, as well as to other developmental and family researchers. These datasets provide a unique and powerful source of information for BG researchers. But much of the information required for biometrical modeling has been hidden, and has required substantial programming effort to uncover-until recently. Our research team has spent over 20 years developing kinship links to genetically inform biometrical modeling. In the most recent release of kinship links from two of the NLSY datasets, the direct kinship indicators included in the 2006 surveys allowed successful and unambiguous linking of over 94 % of the potential pairs. In this paper, we provide details for research teams interested in using the NLSY data portfolio to conduct BG (and other family-oriented) research.

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods.

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.

Behavior problems and timing of menarche: a developmental longitudinal biometrical analysis using the NLSY-Children data.

A powerful longitudinal data source, the National Longitudinal Survey of Youth Children data, allows measurement of behavior problems (BP) within a developmental perspective linking them to menarcheal timing (MT). In a preliminary analysis, we evaluate the bivariate relationships between BP measured at different developmental periods and the timing of menarche. Correlations were not consistent with any correlational/causal relationship between BP and MT. In the major part of our study, MT was used to moderate the developmental trajectory of BP, within a genetically-informed design. Girls reaching menarche early had behavior problem variance accounted for by the shared environment; those reaching menarche with average/late timing had behavior problem differences accounted for by genetic variance. Our findings match previous empirical results in important ways, and also extend those results. A theoretical interpretation is offered in relation to a theory linking genetic/shared environmental variance to flexibility and choices available within the family in relation to BP.

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Model Conditioned Data Elasticity in Path Analysis: Assessing the "Confoundability" of Model/Data Characteristics.

Much research has been directed at the validity of fit indices in Path Analysis and Structural Equation Modeling (e.g., Browne, MacCallum, Kim, Andersen, & Glaser, 2002 ; Heene, Hilbert, Draxler, Ziegler, & Bühner, 2011 ; Hu & Bentler, 1999 ; Marsh, Hau, & Wen, 2004 ). Recent developments (e.g., Preacher, 2006 ; Roberts & Pashler, 2000 , 2002 ) have encouraged researchers to investigate other criteria for comparing models, including model complexity. What has not been investigated is the inherent ability of a particular data set to be fitted with a constrained set of randomly generated linear models, which we call Model Conditioned Data Elasticity (DE). In this article we show how DE can be compared with the problem of equivalent models and a more general problem of the "confoundability" of data/model combinations (see MacCallum, Wegener, Uchino, & Fabrigar, 1993 ). Using the DE package in R, we show how DE can be assessed through automated computer searches. Finally, we discuss how DE fits within the controversy surrounding the use of fit statistics.

The Flynn Effect within Subgroups in the U.S.: Gender, Race, Income, Education, and Urbanization Differences in the NLSY-Children Data.

Although the Flynn Effect has been studied widely across cultural, geographic, and intellectual domains, and many explanatory theories have been proposed, little past research attention has been paid to subgroup differences. Rodgers and Wänström (2007) identified an aggregate-level Flynn Effect (FE) at each age between 5 and 13 in the Children of the National Longitudinal Survey of Youth (NLSYC) PIAT-Math data. FE patterns were not obtained for Reading Recognition, Reading Comprehension, or Digit Span, consistent with past FE research suggesting a closer relationship to fluid intelligence measures of problem solving and analytic reasoning than to crystallized measures of verbal comprehension and memory. These prior findings suggest that the NLSYC data can be used as a natural laboratory to study more subtle FE patterns within various demographic subgroups. We test for subgroup Flynn Effect differences by gender, race/ethnicity, maternal education, household income, and urbanization. No subgroups differences emerged for three demographic categories. However, children with more educated (especially college educated) mothers and/or children born into higher income households had an accelerated Flynn effect in their PIAT-M scores compared to cohort peers with lower educated mothers or lower income households. We interpret both the positive and the null findings in relation to previous theoretical explanations.