Explore the vast range of titles available.

Questions: Is there evidence for biotic homogenization of upland vegetation? Do the magnitude and nature of floristic and compositional change vary between vegetation types? What can be inferred about the drivers responsible for the observed changes? Location: Upland heath, mire and grassland communities of the northwest Highlands of Scotland, UK. Methods: We re-survey plots first described in a phytosociological study of 1956—1958 to assess the changes in plant species composition over the last 50 yr in five major upland vegetation types. Using a combination of multivariate analysis, dissimilarity measures, diversity metrics and published data on species attributes; we quantify, characterize and link potential drivers of environmental change with the observed changes in species composition. Results: Grassland and heath vegetation declined in species richness and variation in community composition, while mires showed little change. Previously distinct vegetation types became more similar in composition, characterized by the increased dominance of generalist upland graminoids and reduced dwarfshrub, forb and lichen cover, although novel assemblages were not apparent. Species with an oceanic distribution increased at the expense of those with an arctic-montane distribution. Temperature, precipitation and acidity were found to be potentially important in explaining changes in species composition: species that had undergone the greatest increases had a preference for warmer, drier and more acidic conditions. Conclusions: The vegetation of the northwest Scottish Highlands has undergone marked biotic homogenization over the last 50 yr, manifested through a loss of various aspects of diversity at the local, community and landscape scales. The magnitude of change varies between vegetation types, although the nature of change shows many similar characteristics. Analyses of species attributes suggest these changes are driven by climate warming and acidification, although over-grazing may also be important. This study highlights the importance of the link between the loss of plant diversity and homogenization at multiple scales, and demonstrates that boreal heath communities are particularly at risk from these processes.

First is probability sampling in which each member of the target population has an equal probability of being selected as a study participant. Important to note is that with probability (random) sampling techniques, there is less risk of bias in the results of the research, and most important, statistical methods such as optimal sample size and sampling error as well as precision of results can be determined. Quota sampling is another form of non-probability sampling in which convenience sampling is layered onto a systematic population segmentation process (often done with “street” interviews in which people are profiled for characteristics that may fit the study objectives for participants).

Despite their disadvantaged generalizability relative to probability samples, nonprobability convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost‐prohibitive and most available probability samples are ill‐suited to examine developmental questions. In lieu of focusing on how to eliminate or sharply reduce reliance on convenience samples within developmental science, here we propose how to augment their advantages when it comes to understanding population effects as well as subpopulation differences. Although all convenience samples have less clear generalizability than probability samples, we argue that homogeneous convenience samples have clearer generalizability relative to conventional convenience samples. Therefore, when researchers are limited to convenience samples, they should consider homogeneous convenience samples as a positive alternative to conventional (or heterogeneous) convenience samples. We discuss future directions as well as potential obstacles to expanding the use of homogeneous convenience samples in developmental science.

Background. Previous reviews have demonstrated a higher risk of suicide attempts for lesbian, gay, and bisexual (LGB) persons (sexual minorities), compared with heterosexual groups, but these were restricted to general population studies, thereby excluding individuals sampled through LGB community venues. Each sampling strategy, however, has particular methodological strengths and limitations. For instance, general population probability studies have defined sampling frames but are prone to information bias associated with underreporting of LGB identities. By contrast, LGB community surveys may support disclosure of sexuality but overrepresent individuals with strong LGB community attachment. Objectives. To reassess the burden of suicide-related behavior among LGB adults, directly comparing estimates derived from population- versus LGB community–based samples. Search methods. In 2014, we searched MEDLINE, EMBASE, PsycInfo, CINAHL, and Scopus databases for articles addressing suicide-related behavior (ideation, attempts) among sexual minorities. Selection criteria. We selected quantitative studies of sexual minority adults conducted in nonclinical settings in the United States, Canada, Europe, Australia, and New Zealand. Data collection and analysis. Random effects meta-analysis and meta-regression assessed for a difference in prevalence of suicide-related behavior by sample type, adjusted for study or sample-level variables, including context (year, country), methods (medium, response rate), and subgroup characteristics (age, gender, sexual minority construct). We examined residual heterogeneity by using τ 2 . Main results. We pooled 30 cross-sectional studies, including 21 201 sexual minority adults, generating the following lifetime prevalence estimates of suicide attempts: 4% (95% confidence interval [CI] = 3%, 5%) for heterosexual respondents to population surveys, 11% (95% CI = 8%, 15%) for LGB respondents to population surveys, and 20% (95% CI = 18%, 22%) for LGB respondents to community surveys ( Figure 1 ). The difference in LGB estimates by sample type persisted after we accounted for covariates with meta-regression. Sample type explained 33% of the between-study variability. Author’s conclusions. Regardless of sample type examined, sexual minorities had a higher lifetime prevalence of suicide attempts than heterosexual persons; however, the magnitude of this disparity was contingent upon sample type. Community-based surveys of LGB people suggest that 20% of sexual minority adults have attempted suicide. Public health implications. Accurate estimates of sexual minority health disparities are necessary for public health monitoring and research. Most data describing these disparities are derived from 2 sample types, which yield different estimates of the lifetime prevalence of suicide attempts. Additional studies should explore the differential effects of selection and information biases on the 2 predominant sampling approaches used to understand sexual minority health.

Mechanical Turk (MTurk) is a common source of research participants within the academic community. Despite MTurk's utility and benefits over traditional subject pools some researchers have questioned whether it is sustainable. Specifically, some have asked whether MTurk workers are too familiar with manipulations and measures common in the social sciences, the result of many researchers relying on the same small participant pool. Here, we show that concerns about non-naivete on MTurk are due less to the MTurk platform itself and more to the way researchers use the platform. Specifically, we find that there are at least 250,000 MTurk workers worldwide and that a large majority of US workers are new to the platform each year and therefore relatively inexperienced as research participants. We describe how inexperienced workers are excluded from studies, in part, because of the worker reputation qualifications researchers commonly use. Then, we propose and evaluate an alternative approach to sampling on MTurk that allows researchers to access inexperienced participants without sacrificing data quality. We recommend that in some cases researchers should limit the number of highly experienced workers allowed in their study by excluding these workers or by stratifying sample recruitment based on worker experience levels. We discuss the trade-offs of different sampling practices on MTurk and describe how the above sampling strategies can help researchers harness the vast and largely untapped potential of the Mechanical Turk participant pool.

Although dependence in effect sizes is ubiquitous, commonly used meta-analytic methods assume independent effect sizes. We describe and illustrate three-level extensions of a mixed effects meta-analytic model that accounts for various sources of dependence within and across studies, because multilevel extensions of meta-analytic models still are not well known. We also present a three-level model for the common case where, within studies, multiple effect sizes are calculated using the same sample. Whereas this approach is relatively simple and does not require imputing values for the unknown sampling covariances, it has hardly been used, and its performance has not been empirically investigated. Therefore, we set up a simulation study, showing that also in this situation, a three-level approach yields valid results: Estimates of the treatment effects and the corresponding standard errors are unbiased.

Accurate estimation of the finite population mean is a fundamental challenge in survey sampling, especially when dealing with large or complex populations. Traditional methods like simple random sampling may not always provide reliable or efficient estimates in such cases. Motivated by this, the current study explores complex sampling techniques to improve the precision and accuracy of mean estimators. Specifically, we employ two-stage and three-stage cluster sampling methods to develop unbiased estimators for the finite population mean. Building upon these, the next phase of the study formulates unbiased mean estimators using stratified two- and three-stage cluster sampling. To further enhance the precision of these estimators, a ranked-set sampling strategy is applied to the secondary and tertiary sampling stages. Additionally, unbiased variance estimators corresponding to the proposed mean estimators are derived. Real-world datasets are utilized to demonstrate the application of these complex survey sampling methodologies, with results showing that the mean estimates derived using ranked set sampling are more accurate than those obtained via simple random sampling.

This study assessed the accuracy of telephone and Internet surveys of probability samples and Internet surveys of non-probability samples of American adults by comparing aggregate survey results against benchmarks. The probability sample surveys were consistently more accurate than the non-probability sample surveys, even after post-stratification with demographics. The non-probability sample survey measurements were much more variable in their accuracy, both across measures within a single survey and across surveys with a single measure. Post-stratification improved the overall accuracy of some of the non-probability sample surveys but decreased the overall accuracy of others.

•Describes the ABCD study aims and design.•Covers issues surrounding estimation of meaningful associations, including population inferences, effect sizes, and control of covariates.•Outlines best practices for reproducible research and reporting of results.•Provides worked examples that illustrate the main points of the paper. The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9–10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

Since its outbreak in January 2020, COVID-19 has quickly spread worldwide and has become a global pandemic. Social media platforms have been recognized as important tools for health-promoting practices in public health, and the use of social media is widespread among the public. However, little is known about the effects of social media use on health promotion during a pandemic such as COVID-19. In this study, we aimed to explore the predictive role of social media use on public preventive behaviors in China during the COVID-19 pandemic and how disease knowledge and eHealth literacy moderated the relationship between social media use and preventive behaviors. A national web-based cross-sectional survey was conducted by a proportionate probability sampling among 802 Chinese internet users (\"netizens\") in February 2020. Descriptive statistics, Pearson correlations, and hierarchical multiple regressions were employed to examine and explore the relationships among all the variables. Almost half the 802 study participants were male (416, 51.9%), and the average age of the participants was 32.65 years. Most of the 802 participants had high education levels (624, 77.7%), had high income >¥5000 (US $736.29) (525, 65.3%), were married (496, 61.8%), and were in good health (486, 60.6%). The average time of social media use was approximately 2 to 3 hours per day (mean 2.34 hours, SD 1.11), and the most frequently used media types were public social media (mean score 4.49/5, SD 0.78) and aggregated social media (mean score 4.07/5, SD 1.07). Social media use frequency (β=.20, P<.001) rather than time significantly predicted preventive behaviors for COVID-19. Respondents were also equipped with high levels of disease knowledge (mean score 8.15/10, SD 1.43) and eHealth literacy (mean score 3.79/5, SD 0.59). Disease knowledge (β=.11, P=.001) and eHealth literacy (β=.27, P<.001) were also significant predictors of preventive behaviors. Furthermore, eHealth literacy (P=.038) and disease knowledge (P=.03) positively moderated the relationship between social media use frequency and preventive behaviors, while eHealth literacy (β=.07) affected this relationship positively and disease knowledge (β=-.07) affected it negatively. Different social media types differed in predicting an individual's preventive behaviors for COVID-19. Aggregated social media (β=.22, P<.001) was the best predictor, followed by public social media (β=.14, P<.001) and professional social media (β=.11, P=.002). However, official social media (β=.02, P=.597) was an insignificant predictor. Social media is an effective tool to promote behaviors to prevent COVID-19 among the public. Health literacy is essential for promotion of individual health and influences the extent to which the public engages in preventive behaviors during a pandemic. Our results not only enrich the theoretical paradigm of public health management and health communication but also have practical implications in pandemic control for China and other countries.