scholarly journals Differences between sons and daughters in the intergenerational transmission of wealth

2019 ◽  
Vol 374 (1780) ◽  
pp. 20180076 ◽  
Author(s):  
Monique Borgerhoff Mulder ◽  
Mary C. Towner ◽  
Ryan Baldini ◽  
Bret A. Beheim ◽  
Samuel Bowles ◽  
...  
Keyword(s):  
Gender Inequality ◽  
Statistical Power ◽  
Small Sample ◽  
Cross Cultural ◽  
Small Scale ◽  
Hunter Gatherers ◽  
Offspring Sex ◽  
Intergenerational Correlations ◽  
Small Scale Farmers ◽  
Small Sample Sizes

Persistent interest lies in gender inequality, especially with regard to the favouring of sons over daughters. Economists are concerned with how privilege is transmitted across generations, and anthropologists have long studied sex-biased inheritance norms. There has, however, been no focused cross-cultural investigation of how parent–offspring correlations in wealth vary by offspring sex. We estimate these correlations for 38 wealth measures, including somatic and relational wealth, from 15 populations ranging from hunter–gatherers to small-scale farmers. Although small sample sizes limit our statistical power, we find no evidence of ubiquitous male bias, at least as inferred from comparing parent–son and parent–daughter correlations. Rather we find wide variation in signatures of sex bias, with evidence of both son and daughter-biased transmission. Further, we introduce a model that helps pinpoint the conditions under which simple mid-point parent–offspring wealth correlations can reveal information about sex-biased parental investment. Our findings are relevant to the study of female-biased kinship by revealing just how little normative descriptors of kinship systems, such as patrilineal inheritance, capture intergenerational correlations in wealth, and how variable parent–son and parent–daughter correlations can be. This article is part of the theme issue ‘The evolution of female-biased kinship in humans and other mammals'.

scholarly journals MISSING VALUE IMPUTATION VIA GRAPH COMPLETION IN QUESTIONNAIRE SCORES FROM PERSONS WITH DEMENTIA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S972-S972
Author(s):  
Chen Kan ◽  
Won Hwa Kim ◽  
Ling Xu ◽  
Noelle L Fields
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Small Sample Size ◽  
Small Sample ◽  
Small Scale ◽  
Assessment And Treatment ◽  
Using Data ◽  
Small Sample Sizes ◽  
Clinical Conditions

Abstract Background: Questionnaires are widely used to evaluate cognitive functions, depression, and loneliness of persons with dementia (PWDs). Successful assessment and treatment of dementia hinge on effective analysis of PWDs’ answers. However, many studies, especially pilot ones, are with small sample sizes. Further, most of them contain missing data as PWDs skip some study sessions due to their clinical conditions. Conventional imputation strategies are not well-suited as bias will be introduced because of insufficient samples. Method: A novel machine learning framework was developed based on harmonic analysis on graphs to robustly handle missing values. Participants were first embedded as nodes in the graph with edges derived by their similarities based on demographic information, activities of daily living, etc. Then, questionnaire scores with missing values were regarded as a function on the nodes, and they were estimated based on spectral analysis of the graph with a smoothness constraint. The proposed approach was evaluated using data from our pilot study of dementia subjects (N=15) with 15% data missing. Result: A few complete variables (binary or ordinal) were available for all participants. For each variable, we randomly removed 5 scores to mimic missing values. With our approach, we could recover all missing values with 90% accuracy on average. We were also able to impute the actual missing values in the dataset within reasonable ranges. Conclusion: Our proposed approach imputes missing values with high accuracy despite the small sample size. The proposed approach will significantly boost statistical power of various small-scale studies with missing data.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

Distinguishing autism from co-existing conditions: a behavioural profiling investigation

2016 ◽  
Vol 2 (1) ◽  
pp. 41-54
Author(s):  
Ashleigh Saunders ◽  
Karen E. Waldie
Keyword(s):  
Statistical Power ◽  
Psychiatric Comorbidity ◽  
Adaptive Functioning ◽  
Autism Spectrum ◽  
Small Sample ◽  
Comorbid Conditions ◽  
Sample Sizes ◽  
Content Type ◽  
Small Sample Sizes ◽  
Comorbid Adhd

Purpose – Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition for which there is no known cure. The rate of psychiatric comorbidity in autism is extremely high, which raises questions about the nature of the co-occurring symptoms. It is unclear whether these additional conditions are true comorbid conditions, or can simply be accounted for through the ASD diagnosis. The paper aims to discuss this issue. Design/methodology/approach – A number of questionnaires and a computer-based task were used in the current study. The authors asked the participants about symptoms of ASD, attention deficit hyperactivity disorder (ADHD) and anxiety, as well as overall adaptive functioning. Findings – The results demonstrate that each condition, in its pure form, can be clearly differentiated from one another (and from neurotypical controls). Further analyses revealed that when ASD occurs together with anxiety, anxiety appears to be a separate condition. In contrast, there is no clear behavioural profile for when ASD and ADHD co-occur. Research limitations/implications – First, due to small sample sizes, some analyses performed were targeted to specific groups (i.e. comparing ADHD, ASD to comorbid ADHD+ASD). Larger sample sizes would have given the statistical power to perform a full scale comparative analysis of all experimental groups when split by their comorbid conditions. Second, males were over-represented in the ASD group and females were over-represented in the anxiety group, due to the uneven gender balance in the prevalence of these conditions. Lastly, the main profiling techniques used were questionnaires. Clinical interviews would have been preferable, as they give a more objective account of behavioural difficulties. Practical implications – The rate of psychiatric comorbidity in autism is extremely high, which raises questions about the nature of the co-occurring symptoms. It is unclear whether these additional conditions are true comorbid conditions, or can simply be accounted for through the ASD diagnosis. Social implications – This information will be important, not only to healthcare practitioners when administering a diagnosis, but also to therapists who need to apply evidence-based treatment to comorbid and stand-alone conditions. Originality/value – This study is the first to investigate the nature of co-existing conditions in ASD in a New Zealand population.

The Identity Status Continuum Revisited

2018 ◽  
Vol 23 (4) ◽  
pp. 289-299 ◽  
Author(s):  
Wim Meeus
Keyword(s):  
Identity Development ◽  
Identity Formation ◽  
Statistical Power ◽  
Developmental Psychology ◽  
Meta Analysis ◽  
Small Sample ◽  
Identity Status ◽  
Identity Statuses ◽  
Identity Maintenance ◽  
Small Sample Sizes

Abstract. The developmental continuum of identity status has been a topic of theoretical debate since the early 1980’s. A recent meta-analysis and recent studies with dual cycle models lead to two conclusions: (1) during adolescence there is systematic identity maturation; (2) there are two continuums of identity status progression. Both continuums show that in general adolescents move from transient identity statuses to identity statuses that mark the relative endpoints of development: from diffusion to closure, and from searching moratorium and moratorium to closure and achievement. This pattern can be framed as development from identity formation to identity maintenance. In Identity Status Interview research using Marcia’s model, not the slightest indication for a continuum of identity development was found. This may be due to the small sample sizes of the various studies leading to small statistical power to detect differences in identity status transitions, as well as developmental inconsistencies in Marcia’s model. Findings from this review are interpreted in terms of life-span developmental psychology.

Growth Need Strength and Context Satisfactions as Moderators of the Relations of the Job Characteristics Model

1992 ◽  
Vol 18 (3) ◽  
pp. 575-593 ◽  
Author(s):  
Robert B. Tiegs ◽  
Lois E. Tetrick ◽  
Yitzhak Fried
Keyword(s):  
Statistical Power ◽  
Job Characteristics ◽  
Job Security ◽  
Small Sample ◽  
Work Outcomes ◽  
Moderating Effects ◽  
Psychological States ◽  
Job Characteristics Model ◽  
Small Sample Sizes ◽  
Overall Job Satisfaction

Empirical investigations of the job characteristics model (JCM; Hackman & Oldham, 1980) have failed to systematically explore the moderating effects of growth need strength (GNS) and context satisfactions (viz., pay, job security, co-worker, and supervision) on the relations among the core job characteristics, critical psychological states, and work outcomes. Previous studies also are criticized for the use of subgroup analytic techniques, low statistical power resulting from small sample sizes (i.e, often less than 200) and/or samples consisting of individuals of relatively homogeneous jobs/occupations. As an attempt to address these deficiencies in the literature, this study examined the moderating effects of GNS and each of the four context satisfactions using a large sample (N = 6405) of employees from a variety of jobs and occupations. Overall, the results of univariate and multivariate hierarchical moderated multiple regression analyses suggest that none of thefive individual difference factors appeared to be viable moderators of any of the relations among job characteristics, psychological states, and three work outcomes (viz., growth satisfaction, overall job satisfaction, and internal motivation). Also, there was no supportive evidence for potential joint moderating effects between GNS and each context satisfaction on the relations of the JCM.

Statistics for Small Groups: The Power of the Pretest

1988 ◽  
Vol 13 (3) ◽  
pp. 142-146 ◽  
Author(s):  
David A. Cole
Keyword(s):  
Statistical Power ◽  
Change Score ◽  
Small Sample ◽  
Analysis Of Covariance ◽  
Small Samples ◽  
Group Differences ◽  
Control Group ◽  
Small Sample Sizes ◽  
Almost All ◽  
Treatment Control Group

In the area of severe-profound retardation, researchers are faced with small sample sizes. The question of statistical power is critical. In this article, three commonly used tests for treatment-control group differences are compared with respect to their relative power: the posttest-only approach, the change-score approach, and an analysis of covariance (ANCOVA) approach. In almost all cases, the ANCOVA approach is the more powerful than the other two, even when very small samples are involved. Finally, a fourth approach involving ANCOVA plus alternate rank assignments is examined and found to be superior even to the ANCOVA approach, especially in small sample cases. Use of slightly more sophisticated statistics in small sample research is recommended.

scholarly journals The inheritance of metabolic flux: expressions for the within-sibship mean and variance given the parental genotypes.

Genetics ◽  
1990 ◽  
Vol 125 (3) ◽  
pp. 655-667
Author(s):  
P J Ward
Keyword(s):  
Statistical Power ◽  
Metabolic Flux ◽  
Genetic Variance ◽  
Small Sample ◽  
Polygenic Inheritance ◽  
Allozymic Variation ◽  
Recent Developments ◽  
Mean And Variance ◽  
Small Sample Sizes ◽  
Adequate Power

Abstract Recent developments have related quantitative trait expression to metabolic flux. The present paper investigates some implications of this for statistical aspects of polygenic inheritance. Expressions are derived for the within-sibship genetic mean and genetic variance of metabolic flux given a pair of parental, diploid, n-locus genotypes. These are exact and hold for arbitrary numbers of gene loci, arbitrary allelic values at each locus, and for arbitrary recombination fractions between adjacent gene loci. The within-sibship, genetic variance is seen to be simply a measure of parental heterozygosity plus a measure of the degree of linkage coupling within the parental genotypes. Approximations are given for the within-sibship phenotypic mean and variance of metabolic flux. These results are applied to the problem of attaining adequate statistical power in a test of association between allozymic variation and inter-individual variation in metabolic flux. Simulations indicate that statistical power can be greatly increased by augmenting the data with predictions and observations on progeny statistics in relation to parental allozyme genotypes. Adequate power may thus be attainable at small sample sizes, and when allozymic variation is scored at a only small fraction of the total set of loci whose catalytic products determine the flux.

scholarly journals Reaching People With Disabilities in Underserved Areas Through Digital Interventions: Systematic Review (Preprint)

2018 ◽  
Author(s):  
Leming Zhou ◽  
Bambang Parmanto
Keyword(s):  
Systematic Review ◽  
Rural Areas ◽  
Objective Assessment ◽  
People With Disabilities ◽  
Small Sample ◽  
Current Status ◽  
Functional Improvement ◽  
Small Scale ◽  
Digital Interventions ◽  
Small Sample Sizes

BACKGROUND People with disabilities need rehabilitation interventions to improve their physical functioning, mental status, and quality of life. Many rehabilitation interventions can be delivered electronically ("digitally") via telehealth systems. For people with disabilities in underserved areas, electronically delivered rehabilitation interventions may be the only feasible service available for them. OBJECTIVE The objective of this study was to evaluate the current status of digital interventions for people with disabilities in remote and underserved areas. METHODS A systematic review was conducted on this topic. Keyword searches in multiple databases (PubMed, CINAHL, and Inspec) were performed to collect articles published in this field. The obtained articles were selected based on our selection criteria. Of the 198 identified articles, 16 duplicates were removed. After a review of the titles and abstracts of the remaining articles, 165 were determined to be irrelevant to this study and were therefore removed. The full texts of the remaining 17 articles were reviewed, and 6 of these articles were removed as being irrelevant to this study. The 11 articles remaining were discussed and summarized by 2 reviewers. RESULTS These 11 studies cover a few types of disabilities, such as developmental disabilities and mobility impairments as well as several types of disability-causing disorders such as stroke, multiple sclerosis, traumatic brain injury, and facio-scapulo-humeral muscular dystrophy. Most of these studies were small-scale case studies and relatively larger-scale cohort studies; the project evaluation methods were mainly pre-post comparison, questionnaires, and interviews. A few studies also performed objective assessment of functional improvement. The intervention technology was mainly videoconferencing. Moreover, 10 of these studies were for people with disabilities in rural areas and 1 was for people in urban communities. CONCLUSIONS A small number of small-scale studies have been conducted on digital interventions for people with disabilities in underserved areas. Although the results reported in these studies were mostly positive, they are not sufficient to prove the effectiveness of telehealth-based digital intervention in improving the situation among people with disabilities because of the small sample sizes and lack of randomized controlled trials.

scholarly journals Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

2021 ◽  
Author(s):  
Aurora Savino ◽  
Niccolò de Marzo ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Statistical Power ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Breast Tumors ◽  
Small Sample ◽  
Bulk Analysis ◽  
Small Sample Sizes ◽  
Microarray Datasets ◽  
User Friendly ◽  
Cellular Components

Background: transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, samples’ heterogeneity and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicate the interpretation of bulk transcriptomic profiles. Methods: we collected 48 microarray datasets of laser capture microdissected breast tumors, and performed a meta-analysis to identify robust lists of genes differentially expressed in these tumors. We created a database with carefully harmonized metadata to be used as a resource for the research community. Results: combining the results of multiple datasets improved the statistical power, and the analysis of stroma and epithelium separately allows identifying genes with different contribution in each compartment. Conclusions: our database can profitably help biomarkers’ discovery and is readily accessible through a user-friendly web interface (https://aurorasavino.shinyapps.io/metalcm/).

Functional Specificity and Sex Differences in the Neural Circuits Supporting the Inhibition of Automatic Imitation

2018 ◽  
Author(s):  
Kohinoor Monish Darda ◽  
Emily E. Butler ◽  
Richard Ramsey
Keyword(s):  
Sex Differences ◽  
Statistical Power ◽  
Small Sample ◽  
Neural Systems ◽  
Specific System ◽  
Analysis Pipeline ◽  
Automatic Imitation ◽  
Domain Specific ◽  
Neural Underpinnings ◽  
Small Sample Sizes

Although humans show an involuntary tendency to copy other people’s actions, which builds rapport between individuals, we do not copy actions indiscriminately. Instead, copying behaviours are guided by a selection mechanism, which inhibits some actions and prioritises others. To date, the neural underpinnings of the inhibition of automatic imitation and differences between the sexes in imitation control are not well understood. Previous studies involved small sample sizes and low statistical power, which produced mixed findings regarding the involvement of domain-general and domain-specific neural architectures. Here, we used data from Experiment 1 (N=28) to perform a power analysis to determine the sample size required for Experiment 2 (N=50; 80% power). Using independent functional localisers and an analysis pipeline that bolsters sensitivity, during imitation control we show clear engagement of the multiple-demand network (domain-general), but no sensitivity in the theory-of-mind network (domain-specific). Weaker effects were observed with regard to sex differences, suggesting that there are more similarities than differences between the sexes in terms of the neural systems engaged during imitation control. In sum, neurocognitive models of imitation require revision to reflect that the inhibition of imitation relies on a domain-general selection system rather than a domain-specific system supporting social cognition.

Sign in / Sign up

Export Citation Format

Share Document