scholarly journals The Harms That Drinkers Cause: Regional Variations Within Countries

2018 ◽  
Vol 7 (2) ◽  
pp. 30-36 ◽  
Author(s):  
Richard W. Wilsnack ◽  
Arlinda F. Kristjanson ◽  
Sharon C. Wilsnack ◽  
Kim Bloomfield ◽  
Ulrike Grittner ◽  
...  
Keyword(s):  
Adverse Effects ◽  
Multilevel Modeling ◽  
Survey Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Drinking Behavior ◽  
Regional Variations ◽  
The Past ◽  
Gender And Age

Aims: Multinational studies of drinking and the harms it may cause typically treat countries as homogeneous. Neglecting variation within countries may lead to inaccurate conclusions about drinking behavior, particularly regarding the harms drinking causes for people other than the drinkers. This study is the first to examine whether drinkers' self-reported harms to others from drinking vary regionally within multiple countries.Design, Setting, and Participants: Analyses draw on survey data from 12,356 drinkers in 46 regions (governmental subunits) within 10 countries, collected as part of the GENACIS project (Wilsnack, Wilsnack, Kristjanson, Vogeltanz‐Holm, & Gmel, 2009).Measures: Drinkers reported on eight harms they may have caused others in the past 12 months because of their drinking. The likelihood of reporting one or more of these eight harms was evaluated by multilevel modeling (respondents nested within regions nested within countries), estimating random effects of country and region, and fixed effects of gender, age, and regional prevalence of drinking.Findings: Reports of causing one or more drinking-related harms to others differed significantly by gender and age, and also differed significantly by regions within countries. Reports did not differ significantly by regional prevalence of drinking.Conclusions: National and multinational evaluations of adverse effects of drinking on persons other than the drinkers should give more attention to how those effects may vary regionally within countries.

scholarly journals A NEW MULTILEVEL MODELING APPROACH FOR CLUSTERED SURVIVAL DATA

2020 ◽  
Vol 36 (4) ◽  
pp. 707-750 ◽  
Author(s):  
Jinfeng Xu ◽  
Mu Yue ◽  
Wenyang Zhang
Keyword(s):  
Multilevel Modeling ◽  
Random Effects ◽  
Survival Data ◽  
Birth Interval ◽  
Fixed Effects ◽  
Estimation Procedure ◽  
Unknown Parameters ◽  
Modeling Approach ◽  
Clustered Survival Data ◽  
The Impact

In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals may become unavoidable when the cluster effects are not scalars. On the other hand, if fixed effects are used, there is a danger of having estimators with large variances because there are too many nuisance parameters involved in the model. In this article, using the idea of the homogeneity pursuit, we propose a new multilevel modeling approach for clustered survival data. The proposed modeling approach does not have the potential computational problem as modeling with random effects, and it also involves far fewer unknown parameters than modeling with fixed effects. We also establish asymptotic properties to show the advantages of the proposed model and conduct intensive simulation studies to demonstrate the performance of the proposed method. Finally, the proposed method is applied to analyze a dataset on the second-birth interval in Bangladesh. The most interesting finding is the impact of some important factors on the length of the second-birth interval variation over clusters and its homogeneous structure.

Proportion of Individuals With Past-Year Suicidal Ideation Who Attempted Suicide Over the Past 10 Years in the United States, and the Influence of Age and Sex

Crisis ◽  
2020 ◽  
pp. 1-5
Author(s):  
Shannon Lange ◽  
Courtney Bagge ◽  
Charlotte Probst ◽  
Jürgen Rehm
Keyword(s):  
United States ◽  
Suicidal Ideation ◽  
Random Effects ◽  
Attempted Suicide ◽  
Suicide Attempts ◽  
The United States ◽  
Individual Level ◽  
The Past ◽  
Meta Analyses ◽  
Age Category

Abstract. Background: In recent years, the rate of death by suicide has been increasing disproportionately among females and young adults in the United States. Presumably this trend has been mirrored by the proportion of individuals with suicidal ideation who attempted suicide. Aim: We aimed to investigate whether the proportion of individuals in the United States with suicidal ideation who attempted suicide differed by age and/or sex, and whether this proportion has increased over time. Method: Individual-level data from the National Survey on Drug Use and Health (NSDUH), 2008–2017, were used to estimate the year-, age category-, and sex-specific proportion of individuals with past-year suicidal ideation who attempted suicide. We then determined whether this proportion differed by age category, sex, and across years using random-effects meta-regression. Overall, age category- and sex-specific proportions across survey years were estimated using random-effects meta-analyses. Results: Although the proportion was found to be significantly higher among females and those aged 18–25 years, it had not significantly increased over the past 10 years. Limitations: Data were self-reported and restricted to past-year suicidal ideation and suicide attempts. Conclusion: The increase in the death by suicide rate in the United States over the past 10 years was not mirrored by the proportion of individuals with past-year suicidal ideation who attempted suicide during this period.

scholarly journals A random effects population dynamics model based on proportions-at-age and removal data for estimating total mortality

2012 ◽  
Vol 69 (11) ◽  
pp. 1881-1893 ◽  
Author(s):  
Verena M. Trenkel ◽  
Mark V. Bravington ◽  
Pascal Lorance
Keyword(s):  
Population Dynamics ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Total Mortality ◽  
Estimation Bias ◽  
Effective Sample Size ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Study Parameter

Catch curves are widely used to estimate total mortality for exploited marine populations. The usual population dynamics model assumes constant recruitment across years and constant total mortality. We extend this to include annual recruitment and annual total mortality. Recruitment is treated as an uncorrelated random effect, while total mortality is modelled by a random walk. Data requirements are minimal as only proportions-at-age and total catches are needed. We obtain the effective sample size for aggregated proportion-at-age data based on fitting Dirichlet-multinomial distributions to the raw sampling data. Parameter estimation is carried out by approximate likelihood. We use simulations to study parameter estimability and estimation bias of four model versions, including models treating mortality as fixed effects and misspecified models. All model versions were, in general, estimable, though for certain parameter values or replicate runs they were not. Relative estimation bias of final year total mortalities and depletion rates were lower for the proposed random effects model compared with the fixed effects version for total mortality. The model is demonstrated for the case of blue ling (Molva dypterygia) to the west of the British Isles for the period 1988 to 2011.

scholarly journals Understanding, Choosing, and Unifying Multilevel and Fixed Effect Approaches

2020 ◽  
pp. 1-20
Author(s):  
Chad Hazlett ◽  
Leonard Wainstein
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Standard Errors ◽  
Point Estimate ◽  
Fe Model ◽  
Grouped Data ◽  
Coefficient Estimates ◽  
Political Science Education ◽  
Robust Standard Errors

Abstract When working with grouped data, investigators may choose between “fixed effects” models (FE) with specialized (e.g., cluster-robust) standard errors, or “multilevel models” (MLMs) employing “random effects.” We review the claims given in published works regarding this choice, then clarify how these approaches work and compare by showing that: (i) random effects employed in MLMs are simply “regularized” fixed effects; (ii) unmodified MLMs are consequently susceptible to bias—but there is a longstanding remedy; and (iii) the “default” MLM standard errors rely on narrow assumptions that can lead to undercoverage in many settings. Our review of over 100 papers using MLM in political science, education, and sociology show that these “known” concerns have been widely ignored in practice. We describe how to debias MLM’s coefficient estimates, and provide an option to more flexibly estimate their standard errors. Most illuminating, once MLMs are adjusted in these two ways the point estimate and standard error for the target coefficient are exactly equal to those of the analogous FE model with cluster-robust standard errors. For investigators working with observational data and who are interested only in inference on the target coefficient, either approach is equally appropriate and preferable to uncorrected MLM.

scholarly journals Association of bisphenol A with puberty timing: a meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hui Meng ◽  
Yunping Zhou ◽  
Yunxia Jiang
Keyword(s):  
Bisphenol A ◽  
Confidence Intervals ◽  
Random Effects ◽  
Strong Correlation ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Study Heterogeneity ◽  
The Relationship ◽  
Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

scholarly journals Cognitive load reduction strategies in questionnaire design

2021 ◽  
pp. 147078532098679
Author(s):  
Kylie Brosnan ◽  
Bettina Grün ◽  
Sara Dolnicar
Keyword(s):  
Cognitive Load ◽  
Data Quality ◽  
Survey Data ◽  
Task Difficulty ◽  
Questionnaire Design ◽  
Load Reduction ◽  
The Past ◽  
Load Theory ◽  
Learning Tasks ◽  
Reduction Strategies

Survey data quality suffers when respondents have difficulty completing complex tasks in questionnaires. Cognitive load theory informed the development of strategies for educators to reduce the cognitive load of learning tasks. We investigate whether these cognitive load reduction strategies can be used in questionnaire design to reduce task difficulty and, in so doing, improve survey data quality. We find that this is not the case and conclude that some of the traditional survey answer formats, such as grid questions, which have been criticized in the past lead to equally good data and do not frustrate respondents more than alternative formats.

scholarly journals Love Thy Neighbor? Relationships between Religion and Racial Intolerance in Europe

2015 ◽  
Vol 8 (4) ◽  
pp. 745-771 ◽  
Author(s):  
Stefanie Doebler
Keyword(s):  
Multilevel Modeling ◽  
Survey Data ◽  
Religious Practice ◽  
Religious Conflict ◽  
European Survey ◽  
Depth Analysis ◽  
Life Force ◽  
Neighbor Relationships ◽  
Racial Intolerance

AbstractThis article examines relationships between religion and racial intolerance across 47 countries by applying multilevel modeling to European survey data and is the first in-depth analysis of moderation of these relationships by European national contexts. The analysis distinguishes a believing, belonging, and practice dimension of religiosity. The results yield little evidence of a link between denominational belonging, religious practice, and racial intolerance. The religiosity dimension that matters most for racial intolerance in Europe is believing: believers in a traditional God and believers in a Spirit/Life Force are decidedly less likely, and fundamentalists are more likely than non-believers to be racially intolerant. National contexts also matter greatly: individuals living in Europe's most religious countries, countries with legacies of ethnic-religious conflict and countries with low GDP are significantly more likely to be racially intolerant than those living in wealthier, secular and politically stable countries. This is especially the case for the religiously devout.

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