Corrections of correlations for range restriction (i.e., selection) and unreliability are common in psychometric work. The current rule of thumb for determining the order in which to apply these corrections looks to the nature of the reliability estimate (i.e., restricted or unrestricted). While intuitive, this rule of thumb is untenable when the correction includes the variable upon which selection is made, as is generally the case. Using classical test theory, we show that it is the nature of the range restriction, not the nature of the available reliability coefficient, that determines the sequence for applying corrections for range restriction and unreliability.

The study of prediction bias is important and the last five decades include research studies that examined whether test scores differentially predict academic or employment performance. Previous studies used ordinary least squares (OLS) to assess whether groups differ in intercepts and slopes. This study shows that OLS yields inaccurate inferences for prediction bias hypotheses. This paper builds upon the criterion-predictor factor model by demonstrating the effect of selection, measurement error, and measurement bias on prediction bias studies that use OLS. The range restricted, criterion-predictor factor model is used to compute Type I error and power rates associated with using regression to assess prediction bias hypotheses. In short, OLS is not capable of testing hypotheses about group differences in latent intercepts and slopes. Additionally, a theorem is presented which shows that researchers should not employ hierarchical regression to assess intercept differences with selected samples.

Multivariate selection can be represented as a linear transformation in a geometric framework. This approach has led to considerable simplification in the study of the effects of selection on factor analysis. In this note this approach is extended to describe the effects of selection on regression analysis and to adjust for the effects of selection using the inverse of the linear transformation.

The validity of a test is often estimated in a nonrandom sample of selected individuals. To accurately estimate the relation between the predictor and the criterion we correct this correlation for range restriction. Unfortunately, this corrected correlation cannot be transformed using Fisher's Z transformation, and asymptotic tests of hypotheses based on small or moderate samples are not accurate. We developed a Fisher r to Z transformation for the corrected correlation for each of two conditions: (a) the criterion data were missing due to selection on the predictor (the missing data were MAR); and (b) the criterion was missing at random, not due to selection (the missing data were MCAR). The two Z transformations were evaluated in a computer simulation. The transformations were accurate, and tests of hypotheses and confidence intervals based on the transformations were superior to those that were not based on the transformations.

Several concepts are introduced and defined: measurement invariance, structural bias, weak measurement invariance, strong factorial invariance, and strict factorial invariance. It is shown that factorial invariance has implications for (weak) measurement invariance. Definitions of fairness in employment/admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness/equity to exist. Implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers.

Corrections of correlations for range restriction (i.e., selection) and unreliability are common in psychometric work. The current rule of thumb for determining the order in which to apply these corrections looks to the nature of the reliability estimate (i.e., restricted or unrestricted). While intuitive, this rule of thumb is untenable when the correction includes the variable upon which selection is made, as is generally the case. Using classical test theory, we show that it is the nature of the range restriction, not the nature of the available reliability coefficient, that determines the sequence for applying corrections for range restriction and unreliability.

Corrections for restriction in range due to explicit selection assume the linearity of regression and homoscedastic array variances. This paper develops a theoretical framework in which the effects of some common forms of violation of these assumptions on the estimation of the unrestricted correlation can be investigated. Simple expressions are derived for both the restricted and corrected correlations in terms of the target (unrestricted) correlation in these situations.

Viruses present an amazing genetic variability. An ensemble of infecting viruses, also called a viral quasispecies, is a cloud of mutants centered around a specific genotype. The simplest model of evolution, whose equilibrium state is described by the quasispecies equation, is the Moran–Kingman model. For the sharp-peak landscape, we perform several exact computations and derive several exact formulas. We also obtain an exact formula for the quasispecies distribution, involving a series and the mean fitness. A very simple formula for the mean Hamming distance is derived, which is exact and does not require a specific asymptotic expansion (such as sending the length of the macromolecules to $\infty$ or the mutation probability to 0). With the help of these formulas, we present an original proof for the well-known phenomenon of the error threshold. We recover the limiting quasispecies distribution in the long-chain regime. We try also to extend these formulas to a general fitness landscape. We obtain an equation involving the covariance of the fitness and the Hamming class number in the quasispecies distribution. Going beyond the sharp-peak landscape, we consider fitness landscapes having finitely many peaks and a plateau-type landscape. Finally, within this framework, we prove rigorously the possible occurrence of the survival of the flattest, a phenomenon which was previously discovered by Wilke et al. (Nature 412, 2001) and which has been investigated in several works (see e.g. Codoñer et al. (PLOS Pathogens 2, 2006), Franklin et al. (Artificial Life 25, 2019), Sardanyés et al. (J. Theoret. Biol. 250, 2008), and Tejero et al. (BMC Evolutionary Biol. 11, 2011)).

Casuarina equisetifolia L. commonly called whistling pine is an economically and industrially important tree species with global significance. Although species possess versatile importance worldwide, efforts imparted for selection and designing a robust model of selection index are inadequate. The selection process, based on quantitative and qualitative traits, identified 15 superior trees from the eastern coastal plain of Odisha. These superior trees showcased exceptional qualitative and quantitative attributes. Correlation analysis highlighted key similarities among various traits like volume and above ground biomass (AGB), volume and diameter at breast height (DBH), DBH and AGB, DBH and Tree Height (TH), crown length (CL), height, AGB and height. Principal component analysis emphasized substantial contributions of traits like DBH, height, CL, crown width, AGB and volume across different clusters. Furthermore, culmination resulted in a comprehensive selection index, integrating both qualitative and quantitative characters, reaching 52.04, signifying superior performance among specific accessions. The current study provides valuable insights into selection and designing optimal selection index of C. equisetifolia, guiding future decisions concerning optimal wood production and resource management.

Research on visual attention has uncovered significant anomalies, and some traditional methods may have inadvertently probed peripheral vision rather than attention. Vision science needs to rethink visual attention from the ground up. To facilitate this, for a year I banned the word “attention” in my lab. This constraint promoted a more precise discussion of attention-related phenomena, capacity limits, and mechanisms. The insights gained lead me to challenge attributing to “attention” those phenomena that can be better explained by perceptual processes, are predictable by an ideal observer model, or that otherwise may not require an additional mechanism. I enumerate a set of critical phenomena in need of explanation. Finally, I propose a unifying theory in which all perception results from performing a task, and tasks face a limit on complexity.

Existing empirical literature provides converging evidence that selective emigration enhances human capital accumulation in the world's poorest countries. However, the within-country distribution of such brain gain effects has received limited attention. Focusing on Senegal, we provide evidence that the brain gain mechanism primarily benefits the wealthiest regions that are internationally connected and have better access to education. Conversely, human capital responses are negligible in regions lacking international connectivity, and even negative in better connected regions with inadequate educational opportunities. These results extend to internal migration, implying that highly vulnerable populations are trapped in the least developed areas.

Rapid increase in the hectarage and agricultural systems that use cover cropping for soil conservation and improvement, soil moisture retention, and weed management has highlighted the need to develop formal breeding programs for cover crop species. Cereal rye (Secale cereale L.) is preferred by many growers due to high biomass production and weed-suppression potential, which is believed to be partially due to allelopathy. Rye germplasm exhibits large variability in allelopathic activity, which could be used to breed rye with enhanced weed suppression. Here, we provide an overview of rye history and breeding and describe a strategy to develop rye lines with increased allelopathic activity. The discussion focuses on ways to deal with important challenges to achieving this goal, including obligate cross-pollination and its consequent high segregation levels and the need to quantify allelopathic activity under field conditions. This review seeks to encourage weed scientists to collaborate with plant breeders and promote the development of cover crop cultivars better suited to reduce weed populations.

This study aimed to investigate the effect of leptin gene polymorphism and some environmental factors on milk production traits. Blood samples from 212 Holstein Friesian dairy cattle reared on a private farm were used. The intron 2 region of the leptin gene was digested with Sau3AI restriction enzyme using the PCR-RFLP method. A and B alleles and AA, AB, and BB genotype frequencies for the Sau3AI polymorphism were determined as 0.8821 and 0.1179, and 0.764, 0.236 and 0.000, respectively. Chi-square analysis revealed that the leptin gene polymorphism followed the Hardy–Weinberg equilibrium, including the absence of animals with the BB genotype. The effect of leptin gene polymorphism on all milk production traits was insignificant. For milk production traits, direct heritability (ha2) varied between 0.03 ± 0.283 (for the dry period) and 0.50 ± 0.183 (for milk conductivity). Regarding the milking time (MT), the estimated breeding values (EBVs) of cattle with the AA genotype were higher than the AB genotype (P < 0.05). As a result of this study, in the selection program, allele or genotype could not be suggested as a marker for milk yield characteristics except for the possible exception of milking time and its relationship to mastitis incidence.