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  • Authors: Harasymchuk, Cheryl; Walker, Deanna L.; Muise, Amy; Impett, Emily A.;

    Spending time with a romantic partner by going on dates is important for promoting closeness in established relationships; however, not all date nights are created equally, and some people might be more adept at planning dates that promote closeness. Drawing from the self-expansion model and relationship goals literature, we predicted that people higher (vs. lower) in approach relationship goals would be more likely to plan dates that are more exciting and, in turn, experience more self-expansion from the date and increased closeness with the partner. In Study 1, people in intimate relationships planned a date to initiate with their partners and forecasted the expected level of self-expansion and closeness from engaging in the date. In Study 2, a similar design was employed, but we also followed up with participants 1 week later to ask about the experience of engaging in their planned dates (e.g., self-expansion, closeness from the date). Taken together, the results suggest that people with higher (vs. lower) approach relationship goals derive more closeness from their dates, in part, because of their greater aptitude for planning dates that are more exciting and promote self-expansion.

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    https://doi.org/10.25384/sage....
    Collection . 2021
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    https://doi.org/10.25384/sage....
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      Collection . 2021
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      https://doi.org/10.25384/sage....
      Collection . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kelly, Christine; Dansereau, Lisette; Sebring, Jennifer; Aubrecht, Katie; +4 Authors

    Abstract Many countries adopted comprehensive national initiatives to promote equity in higher education with the goal of transforming the culture of research. Major health research funders are supporting this work through calls for projects that focus on equity, resulting in a proliferation of theoretical frameworks including “intersectionality,” “health equity,” and variations of equity, diversity and inclusion, or EDI. This commentary is geared at individual principal investigators and health research teams who are developing research proposals and want to consider equity issues in their research, perhaps for the first time. We present histories and definitions of three commonly used frameworks: intersectionality, health equity, and EDI. In the context of health research, intersectionality is a methodology (a combination of epistemology and techniques) that can identify the relationships among individual identities and systems of oppression; however, it should also be used internally by research teams to reflect on the production of knowledge. Health equity is a societal goal that operationalizes the social determinants of health to document and address health disparities at the population level. EDI initiatives measure and track progress within organizations or teams and are best suited to inform the infrastructure and human resourcing “behind the scenes” of a project. We encourage researchers to consider these definitions and strive to tangibly move health research towards equity both in the topics we study and in the ways we do research.

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  • A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview

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  • Authors: Dall’Olio, Filippo; Vakratsas, Demetrios;

    This study provides a comprehensive assessment of the impact of advertising creative strategy (ACS) on advertising elasticity, founded on an integrative framework that distinguishes between the function (content) and form (execution) of an advertising creative. The authors evaluate function using a three-dimensional representation of content (experience, affect, cognition), whereas the representation of form accounts for both executional elements and the use of creative templates. The distinction between function and form allows for the investigation of potential synergies between content and execution, previously unaccounted for in the literature. The ACS framework also facilitates the calculation of composite metrics that capture holistic aspects of the creative strategy, such as focus (i.e., the extent of the emphasis on a specific content dimension) and variation (i.e., changes in content and execution over time). The empirical application focuses on a dynamic linear model analysis of 2,251 television advertising creatives from 91 brands in 16 consumer packaged goods categories. The findings show that for function, experiential content has the greatest effect on elasticity, followed by cognitive and affective content. Function and form produce synergies that advertisers can leverage to increase returns. Finally, focus, variation, and the use of templates increase advertising elasticity.

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    https://doi.org/10.25384/sage....
    Collection . 2022
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  • Authors: Saunders, Jessica F.; Zak, Michelle; Matejko, Emily; Kassan, Anusha; +2 Authors

    Many modern emerging adults undertake the task of identity development while navigating life on a post-secondary campus, where they assimilate to new social and learning environments. Emerging adult newcomers (i.e. immigrants) must navigate additional developmental challenges as they reconcile their cultural, ethnic, and personal identity development simultaneously while facing systemic barriers to post-secondary integration. We employed an arts-based engagement ethnography to investigate the post-secondary integration experiences of 10 emerging adults from a person-first perspective. Through cultural probes, individual semi-structured interviews, and focus groups, we identified four key structures to participants’ integration experience: fitting in (through assimilation and accommodation), biculturalism, managing familial expectations, and being a newcomer in the classroom. This research clarifies the key experiences shaping young newcomer identity development and highlights the profound ways in which young newcomers negotiate and reconcile their intersecting identities while integrating into new education contexts following migration.

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    https://doi.org/10.25384/sage....
    Collection . 2021
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    https://doi.org/10.25384/sage....
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      Collection . 2021
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      Collection . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Schmidtke, Daniel; Gagné, Christina L.; Kuperman, Victor; Spalding, Thomas L.; +1 Authors

    Previous research has shown that compound word recognition involves selecting a relational meaning (e.g. “box for letters” for letterbox) out of a set of competing relational meanings for the same compound. We conducted five experiments to investigate the role of competition between relational meanings across visual and auditory compound word processing. In Experiment 1 conceptual relations judgments were collected for 604 English compound words. From this database we computed an information-theoretic measure of competition between conceptual relations – entropy of conceptual relations. Experiments 2 and 3 report that greater entropy (i.e. increased competition) among a set of conceptual relations leads to longer latencies for compounds in auditory lexical decision. Experiments 4 and 5 demonstrate the same result in two visual lexical decision studies. These findings provide evidence that relational meanings are constructed and evaluated during compound recognition, regardless of whether compounds are recognised via auditory or visual input.

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    Dataset . 2018
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    Dataset . 2018
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      Dataset . 2018
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      Dataset . 2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: McIntyre, Laureen; Laurie-Ann Hellsten; Bidonde, Julia; Boden, Catherine; +1 Authors

    Abstract Background The majority of a child’s language development occurs in the first 5 years of life when brain development is most rapid. There are significant long-term benefits to supporting all children’s language and literacy development such as maximizing their developmental potential (i.e., cognitive, linguistic, social-emotional), when children are experiencing a critical period of development (i.e., early childhood to 9 years of age). A variety of people play a significant role in supporting children’s language development, including parents, guardians, family members, educators, and/or speech-language pathologists. Speech-language pathologists and educators are the professionals who predominantly support children’s language development in order for them to become effective communicators and lay the foundation for later developing literacy skills (i.e., reading and writing skills). Therefore, these professionals need formal and informal assessments that provide them information on a child’s understanding and/or use of the increasingly complex aspects of language in order to identify and support the receptive and expressive language learning needs of diverse children during their early learning experiences (i.e., aged 1.5 to 9 years). However, evidence on what methods and tools are being used is lacking. Methods The authors will carry out a scoping review of the literature to identify studies and map the receptive and expressive English language assessment methods and tools that have been published and used since 1980. Arksey and O’Malley’s (2005) six-stage approach to conducting a scoping review was drawn upon to design the protocol for this investigation: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consultation. Discussion This information will help these professionals identify and select appropriate assessment methods or tools that can be used to support development and/or identify areas of delay or difficulty and plan, implement, and monitor the progress of interventions supporting the development of receptive and expressive language skills in individuals with diverse language needs (e.g., typically developing children, children with language delays and disorders, children learning English as a second or additional language, Indigenous children who may be speaking dialects of English). Researchers plan to evaluate the effectiveness of the assessment methods or tools identified in the scoping review as an extension of this study.

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    Collection . 2017
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    Collection . 2017
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    Authors: Alexander, Jake M.; Atwater, Daniel Z.; Colautti, Robert I.; Hargreaves, Anna L.;

    This file contains code to model adaptive trade-offs across a species' range, and to produce Figs. 2 and 3.

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  • Authors: Hamm, Jeremy M.; Perry, Raymond P.; Chipperfield, Judith G.; Hladkyj, Steve; +2 Authors

    Despite increased emphasis on educating students in science, technology, engineering, and mathematics (STEM) disciplines, nearly half of U.S. college students who enroll in these programs fail to graduate with STEM degrees. Using archival data from the Motivation and Academic Achievement Database, we tested whether a motivation intervention to reframe causal attributions for academic setbacks improved graduation rates for college students in STEM disciplines (N = 496). Results showed that the intervention increased the odds of 8-year graduation for students who were at risk of college dropout. Findings highlight the potential of theory-informed psychological interventions to increase persistence to graduation for at-risk students in STEM fields.

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    Authors: Völter, Christoph J.; Tinklenberg, Brandon; Call, Josep; Seed, Amanda M.;

    Cognitive abilities cannot be measured directly. What we can measure is individual variation in task performance. In this paper, we first make the case for why we should be interested in mapping individual differences in task performance on to particular cognitive abilities: we suggest that it is crucial for examining the causes and consequences of variation both within and between species. As a case study, we examine whether multiple measures of inhibitory control for non-human animals do indeed produce correlated task performance; however, no clear pattern emerges that would support the notion of a common cognitive ability underpinning individual differences in performance. We advocate a psychometric approach involving a three-step programme to make theoretical and empirical progress: first, we need tasks that reveal signature limits in performance. Second, we need to assess the reliability of individual differences in task performance. Third, multi-trait multi-method test batteries will be instrumental in validating cognitive abilities. Together, these steps will help us to establish what varies between individuals that could impact their fitness and ultimately shape the course of the evolution of animal minds. Finally, we propose executive functions, including working memory, inhibitory control and attentional shifting, as a sensible starting point for this endeavour.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive ability’.

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698 Research products
  • Authors: Harasymchuk, Cheryl; Walker, Deanna L.; Muise, Amy; Impett, Emily A.;

    Spending time with a romantic partner by going on dates is important for promoting closeness in established relationships; however, not all date nights are created equally, and some people might be more adept at planning dates that promote closeness. Drawing from the self-expansion model and relationship goals literature, we predicted that people higher (vs. lower) in approach relationship goals would be more likely to plan dates that are more exciting and, in turn, experience more self-expansion from the date and increased closeness with the partner. In Study 1, people in intimate relationships planned a date to initiate with their partners and forecasted the expected level of self-expansion and closeness from engaging in the date. In Study 2, a similar design was employed, but we also followed up with participants 1 week later to ask about the experience of engaging in their planned dates (e.g., self-expansion, closeness from the date). Taken together, the results suggest that people with higher (vs. lower) approach relationship goals derive more closeness from their dates, in part, because of their greater aptitude for planning dates that are more exciting and promote self-expansion.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Kelly, Christine; Dansereau, Lisette; Sebring, Jennifer; Aubrecht, Katie; +4 Authors

    Abstract Many countries adopted comprehensive national initiatives to promote equity in higher education with the goal of transforming the culture of research. Major health research funders are supporting this work through calls for projects that focus on equity, resulting in a proliferation of theoretical frameworks including “intersectionality,” “health equity,” and variations of equity, diversity and inclusion, or EDI. This commentary is geared at individual principal investigators and health research teams who are developing research proposals and want to consider equity issues in their research, perhaps for the first time. We present histories and definitions of three commonly used frameworks: intersectionality, health equity, and EDI. In the context of health research, intersectionality is a methodology (a combination of epistemology and techniques) that can identify the relationships among individual identities and systems of oppression; however, it should also be used internally by research teams to reflect on the production of knowledge. Health equity is a societal goal that operationalizes the social determinants of health to document and address health disparities at the population level. EDI initiatives measure and track progress within organizations or teams and are best suited to inform the infrastructure and human resourcing “behind the scenes” of a project. We encourage researchers to consider these definitions and strive to tangibly move health research towards equity both in the topics we study and in the ways we do research.

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  • A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview

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  • Authors: Dall’Olio, Filippo; Vakratsas, Demetrios;

    This study provides a comprehensive assessment of the impact of advertising creative strategy (ACS) on advertising elasticity, founded on an integrative framework that distinguishes between the function (content) and form (execution) of an advertising creative. The authors evaluate function using a three-dimensional representation of content (experience, affect, cognition), whereas the representation of form accounts for both executional elements and the use of creative templates. The distinction between function and form allows for the investigation of potential synergies between content and execution, previously unaccounted for in the literature. The ACS framework also facilitates the calculation of composite metrics that capture holistic aspects of the creative strategy, such as focus (i.e., the extent of the emphasis on a specific content dimension) and variation (i.e., changes in content and execution over time). The empirical application focuses on a dynamic linear model analysis of 2,251 television advertising creatives from 91 brands in 16 consumer packaged goods categories. The findings show that for function, experiential content has the greatest effect on elasticity, followed by cognitive and affective content. Function and form produce synergies that advertisers can leverage to increase returns. Finally, focus, variation, and the use of templates increase advertising elasticity.

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  • Authors: Saunders, Jessica F.; Zak, Michelle; Matejko, Emily; Kassan, Anusha; +2 Authors

    Many modern emerging adults undertake the task of identity development while navigating life on a post-secondary campus, where they assimilate to new social and learning environments. Emerging adult newcomers (i.e. immigrants) must navigate additional developmental challenges as they reconcile their cultural, ethnic, and personal identity development simultaneously while facing systemic barriers to post-secondary integration. We employed an arts-based engagement ethnography to investigate the post-secondary integration experiences of 10 emerging adults from a person-first perspective. Through cultural probes, individual semi-structured interviews, and focus groups, we identified four key structures to participants’ integration experience: fitting in (through assimilation and accommodation), biculturalism, managing familial expectations, and being a newcomer in the classroom. This research clarifies the key experiences shaping young newcomer identity development and highlights the profound ways in which young newcomers negotiate and reconcile their intersecting identities while integrating into new education contexts following migration.

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    Collection . 2021
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    https://doi.org/10.25384/sage....
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      Collection . 2021
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      https://doi.org/10.25384/sage....
      Collection . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Schmidtke, Daniel; Gagné, Christina L.; Kuperman, Victor; Spalding, Thomas L.; +1 Authors

    Previous research has shown that compound word recognition involves selecting a relational meaning (e.g. “box for letters” for letterbox) out of a set of competing relational meanings for the same compound. We conducted five experiments to investigate the role of competition between relational meanings across visual and auditory compound word processing. In Experiment 1 conceptual relations judgments were collected for 604 English compound words. From this database we computed an information-theoretic measure of competition between conceptual relations – entropy of conceptual relations. Experiments 2 and 3 report that greater entropy (i.e. increased competition) among a set of conceptual relations leads to longer latencies for compounds in auditory lexical decision. Experiments 4 and 5 demonstrate the same result in two visual lexical decision studies. These findings provide evidence that relational meanings are constructed and evaluated during compound recognition, regardless of whether compounds are recognised via auditory or visual input.

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    Dataset . 2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: McIntyre, Laureen; Laurie-Ann Hellsten; Bidonde, Julia; Boden, Catherine; +1 Authors

    Abstract Background The majority of a child’s language development occurs in the first 5 years of life when brain development is most rapid. There are significant long-term benefits to supporting all children’s language and literacy development such as maximizing their developmental potential (i.e., cognitive, linguistic, social-emotional), when children are experiencing a critical period of development (i.e., early childhood to 9 years of age). A variety of people play a significant role in supporting children’s language development, including parents, guardians, family members, educators, and/or speech-language pathologists. Speech-language pathologists and educators are the professionals who predominantly support children’s language development in order for them to become effective communicators and lay the foundation for later developing literacy skills (i.e., reading and writing skills). Therefore, these professionals need formal and informal assessments that provide them information on a child’s understanding and/or use of the increasingly complex aspects of language in order to identify and support the receptive and expressive language learning needs of diverse children during their early learning experiences (i.e., aged 1.5 to 9 years). However, evidence on what methods and tools are being used is lacking. Methods The authors will carry out a scoping review of the literature to identify studies and map the receptive and expressive English language assessment methods and tools that have been published and used since 1980. Arksey and O’Malley’s (2005) six-stage approach to conducting a scoping review was drawn upon to design the protocol for this investigation: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; (5) collating, summarizing, and reporting the results; and (6) consultation. Discussion This information will help these professionals identify and select appropriate assessment methods or tools that can be used to support development and/or identify areas of delay or difficulty and plan, implement, and monitor the progress of interventions supporting the development of receptive and expressive language skills in individuals with diverse language needs (e.g., typically developing children, children with language delays and disorders, children learning English as a second or additional language, Indigenous children who may be speaking dialects of English). Researchers plan to evaluate the effectiveness of the assessment methods or tools identified in the scoping review as an extension of this study.

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    Collection . 2017
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    Collection . 2017
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      Collection . 2017
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Alexander, Jake M.; Atwater, Daniel Z.; Colautti, Robert I.; Hargreaves, Anna L.;

    This file contains code to model adaptive trade-offs across a species' range, and to produce Figs. 2 and 3.

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    Dataset . 2022
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  • Authors: Hamm, Jeremy M.; Perry, Raymond P.; Chipperfield, Judith G.; Hladkyj, Steve; +2 Authors

    Despite increased emphasis on educating students in science, technology, engineering, and mathematics (STEM) disciplines, nearly half of U.S. college students who enroll in these programs fail to graduate with STEM degrees. Using archival data from the Motivation and Academic Achievement Database, we tested whether a motivation intervention to reframe causal attributions for academic setbacks improved graduation rates for college students in STEM disciplines (N = 496). Results showed that the intervention increased the odds of 8-year graduation for students who were at risk of college dropout. Findings highlight the potential of theory-informed psychological interventions to increase persistence to graduation for at-risk students in STEM fields.

    https://doi.org/10.2...arrow_drop_down
    https://doi.org/10.25384/sage....
    Collection . 2020
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    https://doi.org/10.25384/sage....
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    Authors: Völter, Christoph J.; Tinklenberg, Brandon; Call, Josep; Seed, Amanda M.;

    Cognitive abilities cannot be measured directly. What we can measure is individual variation in task performance. In this paper, we first make the case for why we should be interested in mapping individual differences in task performance on to particular cognitive abilities: we suggest that it is crucial for examining the causes and consequences of variation both within and between species. As a case study, we examine whether multiple measures of inhibitory control for non-human animals do indeed produce correlated task performance; however, no clear pattern emerges that would support the notion of a common cognitive ability underpinning individual differences in performance. We advocate a psychometric approach involving a three-step programme to make theoretical and empirical progress: first, we need tasks that reveal signature limits in performance. Second, we need to assess the reliability of individual differences in task performance. Third, multi-trait multi-method test batteries will be instrumental in validating cognitive abilities. Together, these steps will help us to establish what varies between individuals that could impact their fitness and ultimately shape the course of the evolution of animal minds. Finally, we propose executive functions, including working memory, inhibitory control and attentional shifting, as a sensible starting point for this endeavour.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive ability’.

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    Collection . 2018
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    Collection . 2018
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      Collection . 2018
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