Relate !!LINK!!
The general code is comparable across all years. Users should note, however, that there are some fundamental differences between the early period (before 1940) and the later period (1940-2010, the ACS, and the PRCS). Group quarters residence is a primary distinction in the relationship variable for the later period, but before 1940 and in the 1940 100% dataset "relationship to head" was recorded regardless of group quarters status. Persons classified as related to the head (codes 1 through 10) in the early period would have been classified in the "Other non-relative" category based on their group quarters status in the later years.
relate
1520s, "to recount, tell," from French relater "refer, report" (14c.) and directly from Latin relatus, used as past participle of referre "bring back, bear back" (see refer), from re- "back, again" + lātus "borne, carried" (see oblate (n.)).
late 14c., referren, "to trace back (a quality, etc., to a first cause or origin), attribute, assign," from Old French referer (14c.) and directly from Latin referre "to relate, refer," literally "to carry back," from re- "back" (see re-) + ferre "to carry, bear" (from PIE root *bher- (1) "to carry," also "to bear children").
We deposited Relate-estimated coalescence rates, allele ages, and p-values for evidence of positive selection for the 1000 Genomes Project here. These were obtained by estimating the joint genealogy of all 1000 GP populations and then extracted the embedded genealogy for each population. For the genealogy of each population, we jointly estimated the population size history and branch lengths. Variants segregating in more than one population therefore have correlated but different allele ages in each population.
Genetic data are useful for estimating the genealogical relationship or relatedness between individuals of unknown ancestry. ML-Relate is a computer program that calculates maximum likelihood estimates of relatedness and relationship. ML-Relate is designed for microsatellite data, and can accommodate null alleles. It uses simulation to perform two types of hypothesis tests.
Input files can have either one or two POP's (to use the GENEPOP term) in them. If the file has one POP, ML-Relate will estimate allele frequencies in the population from the individuals whose relatedness is being estimated. If the file has two POP's the first POP will be used to estimate allele frequencies and the second POP should contain the individuals to analzye. See the user's manual for more details.
Sustained attentional control is critical for everyday tasks and success in school and employment. Understanding gender differences in sustained attentional control, and their potential sources, is an important goal of psychology and neuroscience and of great relevance to society. We used a large web-based sample (n = 21,484, from testmybrain.org) to examine gender differences in sustained attentional control. Our sample included participants from 41 countries, allowing us to examine how gender differences in each country relate to national indices of gender equality. We found significant gender differences in certain aspects of sustained attentional control. Using indices of gender equality, we found that overall sustained attentional control performance was lower in countries with less equality and that there were greater gender differences in performance in countries with less equality. These findings suggest that creating sociocultural conditions which value women and men equally can improve a component of sustained attention and reduce gender disparities in cognition.
Gender differences in cognition have been a source of curiosity and conflict for decades. Most known gender differences have small effect sizes, though some isolated examples such as mental rotation are in the moderate range [1]. Some differences (e.g., math ability, [2])have diminished significantly over the last 30 years, presumably due to the changes in social constructs that were driving the inequality [1]. Furthermore, some studies show differences present in majority ethnic groups yet absent in minority ethnic groups. For example, among Caucasian American teenagers, more boys than girls score in the 99th percentile of mathematics achievement, but the opposite is true of Asian Americans [3]. Gender differences may be absent in privileged social classes (e.g., male advantage in vocabulary in lower-caste but not upper-caste Indian children, [4]) or smaller in countries with greater gender equality [5]. These changes over time and differences between ethnic/sociocultural groups challenge the notion that all gender differences in cognition are innate, and increase the likelihood that many are driven, at least in part, by social variables. The current study examined gender differences in sustained attentional control across a large sample, and then correlated these differences with sociocultural conditions across countries in order to better understand the potential sources of the differences.
We restricted our data set to include only participants whose country location (from IP address) was recorded during testing (N = 16,606, see S1 Table for details). Each of these 16,606 participants was assigned SIGI/HDI/female-male labor force participation/poverty scores based on their country. We used mixed effects models, with a random effect for country and fixed effects for gender, each index, and the interaction between gender and each index. We found that three of our four indices (excluding poverty) were significantly related to gradCPT performance (see Table 3). In particular, less human development and gender equality were associated with slower reaction times, higher CV (more variability), more omission errors and, somewhat paradoxically, slightly fewer commission errors. There was also a significant interaction between gender and three of the four sociocultural indices within omission and commission errors, demonstrating that although overall average performance was affected by sociocultural conditions, men and women were not affected to the same degree (Table 4). There were no significant interactions between index and gender within reaction time and CV, and notably, there was no significant interaction between poverty and gender in any variable.
Though both reaction time and CV showed significant, moderate-sized gender differences, these differences did not correlate with indices of gender equality. Previous work has similarly shown that men have faster, less variable reaction times than women on continuous performance tasks [19]. These gender differences could be less susceptible to environmental conditions than gender differences in error rates. Though the current results cannot rule out a biological interpretation, a mounting body of evidence suggests that biological sources of gender differences in cognition are less common than previously thought. For example, the gender gap in mathematical ability, once considered to demonstrate a biological difference in cognition, has been closing for the last 20 years and is now considered to be very small or non-existent [3]. Although our results showed that gender differences in reaction time or CV were present across nations and across the lifespan, we cannot rule out the possibility that they are affected by environmental conditions we did not measure or which could not be captured at the nation-wide level.
Relationships are a dynamic, flexible way to combine data from multiple tables for analysis. A relationship describes how two tables relate to each other, based on common fields, but does not merge the tables together. When a relationship is created between tables, the tables remain separate, maintaining their individual level of detail and domains.
After you drag the first table to the top-level canvas of the data source, each new table that you drag to the canvas must be related to an existing table. When you create relationships between tables in the logical layer, you are building the data model for your data source.
After you have built your multi-table, related data source, you can dive into exploring that data. For more information, see How Analysis Works for Multi-table Data Sources that Use Relationships and Troubleshooting multi-table analysis.
A relationship describes how two independent tables relate to each other but does not merge the tables together. This avoids the data duplication and filtering issues that might occur in a join and can make working with your data easier.
Linking various pieces of information together is a vital function of CRM. Sugar provides three different ways you can create connections between modules: relationships, relate fields, and flex relate fields.
For more information about each of these three types of connections, including a more thorough description of flex relate fields, please refer to the article Introduction to Relationships and Relate Fields.
Explore our new videos series RELATE, in which we take you to astounding projects around the world, where light and architecture harmonise. We talk to inspiring architects and lighting designers that elaborate on their approach for the project, their design process and road to realisation. Get inspired to imagine, create and relate. 041b061a72