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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.

Objectives This paper models cognitive aging, across mid and late life, and estimates birth cohort and sex differences in both initial levels and aging trajectories over time in a sample with multiple cohorts and a wide span of ages. Methods The data used in this study came from the first 9 waves of the English Longitudinal Study of Ageing, spanning 2002–2019. There were n = 76,014 observations (proportion male 45%). Dependent measures were verbal fluency, immediate recall, delayed recall, and orientation. Data were modeled using a Bayesian logistic growth curve model. Results Cognitive aging was substantial in 3 of the 4 variables examined. For verbal fluency and immediate recall, males and females could expect to lose about 30% of their initial ability between the ages of 52 and 89. Delayed recall showed a steeper decline, with males losing 40% and females losing 50% of their delayed recall ability between ages 52 and 89 (although females had a higher initial level of delayed recall). Orientation alone was not particularly affected by aging, with less than a 10% change for either males or females. Furthermore, we found cohort effects for initial ability level, with particularly steep increases for cohorts born between approximately 1930 and 1950. Discussion These cohort effects generally favored later-born cohorts. Implications and future directions are discussed. [ABSTRACT FROM AUTHOR]

In this chapter, the authors identify and review a number of key features in the successful development and maintenance of a PhD program delivered at a distance. The University of Oklahoma's PhD program in organizational leadership was developed in the early 1990s and delivered (primarily, but not completely) to military personnel and families through a contract with the U.S. Army. The authors identify, review, and evaluate the features that have resulted in a successful distance-oriented PhD program. They begin with a short case study of successful PhD candidates.

We propose, develop, and evaluate the black ink-red ink (BIRI) method of testing. This approach uses two different methods within the same test administration setting, one that matches recognition learning and the other that matches recall learning. Students purposively define their own tradeoff between the two approaches. Evaluation of the method in several different class content and class format settings demonstrates the value of the method in actual classroom settings. In particular, the BIRI approach can substantially reduce test anxiety in accelerated adult-learning settings. (Contains 2 notes.)

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 Wanstrom (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. (Contains 2 tables and 6 figures.)

A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rule-based framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models. (Contains 9 footnotes.)

The Flynn Effect [Flynn, J. R. (1984). The mean IQ of Americans: Massive gains 1932 to 1978. Psychological Bulletin 95, 29-51.] is an increase in IQ of around 0.33 points per year, observed in developed (and some developing) countries during the past century. It emerges from problem solving and other non-verbal components of IQ. The cause has been argued and theories proposed. Rodgers [Rodgers, J. L. (1998). A critique of the Flynn Effect: Massive IQ gains, methodological artifacts, or both? Intelligence 26, 337-356.] noted that the search for causes has preceded specification of the nature of the effect. Our study uses a national sample of U.S. children to test for the Flynn Effect in PIAT-Math, PIAT-Reading Recognition, PIAT-Reading Comprehension, Digit Span, and PPVT. An effect of the predicted magnitude was observed for PIAT-Math when maternal IQ was controlled. This finding in a large representative sample with thousands of variables supports more careful evaluation of the Flynn Effect, in demographic, geographic, environmental, and biological domains.

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. [ABSTRACT FROM AUTHOR]

The Oklahoma City bombing in April 1995 was an act of terrorism that had many potential influences on the city and state, including influences on families. We analyzed divorce data from 1985 to 2000 for all 77 counties in Oklahoma to assess the divorce response to the Oklahoma City bombing. Our prediction was that divorce rates in Oklahoma would decrease in response to the bombing, a prediction derived from two different theoretical orientations, terror management theory and attachment theory. We test this prediction in the context of two relatively powerful quasi-experimental designs, an interrupted time series design, and a non-equivalent control group design. We analyze the time series data with polynomial regression. Results suggested that there were lower observed divorce rates following the Oklahoma City bombing than the prevailing 10-year cubic divorce trend would have predicted, with the effect dampening over time. We analyzed the non-equivalent control group design using an analysis of covariance ANCOVA linear model to compare counties in and around Oklahoma City to those farther away, and to compare metropolitan to non-metropolitan counties in Oklahoma. In each case, divorces were lower in the predicted directions.

Describes why birth order interests both parents and researchers, discussing what really causes apparent birth order effects on intelligence, examining problems with using cross-sectional intelligence data, and noting how to move beyond cross-sectional inferences. Explains the admixture hypothesis, which finds that family size is much more influential in children's intelligence than birth order. (SM)

Investigated the relationship between birth order, family size, and intelligence quotient (IQ), evaluating sibling data from the National Longitudinal Survey of Youth and comparing results with those from other studies using within-family data. Results indicated that although low IQ parents were making large families, large families were not making low IQ children. Similarly, the belief that birth order affects intelligence is a methodological illusion. (SM)

Defines a sampling taxonomy that shows the differences between and relationships among the bootstrap, the jackknife, and the randomization test. Demonstrates the usefulness of the taxonomy for teaching the goals and purposes of resampling schemes and presents univariate and multivariate examples. (SLD)