A Study of Factor Effects in Data From Factorial Experiments

2005 ◽  
Author(s):  
Xiang Li ◽  
Daniel D. Frey
Keyword(s):  
Robust Design ◽  
Meta Analysis ◽  
System Response ◽  
Data Sets ◽  
Engineering Systems ◽  
Factorial Experiments ◽  
Additional Increase ◽  
Efficient Planning ◽  
Effect Sparsity ◽  
Factor Interactions

This paper documents a meta-analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among factor effects and multi-factor interactions. Such regularities are known to be critical to efficient planning and analysis of experiments and to robust design of engineering systems. Three previously observed properties are analyzed — effect sparsity, hierarchy, and heredity. A new regularity is introduced and shown to be statistically significant. It is shown that a preponderance of active two-factor interaction effects are synergistic, meaning that when main effects are used to increase the system response, the interaction provides an additional increase. The potential implications for robust design are discussed.

Race-Related Association between APOE Genotype and Alzheimer’s Disease: A Systematic Review and Meta-Analysis

2021 ◽  
pp. 1-10
Author(s):  
Wei Qin ◽  
Wenwen Li ◽  
Qi Wang ◽  
Min Gong ◽  
Tingting Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Black People ◽  
Late Onset ◽  
Meta Analysis ◽  
Group Analysis ◽  
Apoe Genotype ◽  
Cochrane Library ◽  
Data Sets ◽  
Apoe Genotypes

Background: The global race-dependent association of Alzheimer’s disease (AD) and apolipoprotein E (APOE) genotype is not well understood. Transethnic analysis of APOE could clarify the role of genetics in AD risk across populations. Objective: This study aims to determine how race and APOE genotype affect the risks for AD. Methods: We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library since 1993 to Aug 25, 2020. A total of 10,395 reports were identified, and 133 were eligible for analysis with data on 77,402 participants. Studies contained AD clinical diagnostic and APOE genotype data. Homogeneous data sets were pooled in case-control analyses. Odds ratios and 95% confidence intervals for developing AD were calculated for populations of different races and APOE genotypes. Results: The proportion of APOE genotypes and alleles differed between populations of different races. Results showed that APOE ɛ4 was a risk factor for AD, whereas APOE ɛ2 protected against it. The effects of APOE ɛ4 and ɛ2 on AD risk were distinct in various races, they were substantially attenuated among Black people. Sub-group analysis found a higher frequency of APOE ɛ4/ɛ4 and lower frequency of APOE ɛ3/ɛ3 among early-onset AD than late-onset AD in a combined group and different races. Conclusion: Our meta-analysis suggests that the association of APOE genotypes and AD differ between races. These results enhance our understanding of APOE-related risk for AD across race backgrounds and provide new insights into precision medicine for AD.

A Systematic Review and Meta-Analysis of the Relation Between Engagement and Achievement in Early Childhood Research

2021 ◽  
pp. 027112142110327
Author(s):  
Esther R. Lindström ◽  
Jason C. Chow ◽  
Kathleen N. Zimmerman ◽  
Hongyang Zhao ◽  
Elise Settanni ◽  
...  
Keyword(s):  
Early Childhood ◽  
Academic Engagement ◽  
Literature Search ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Data Sets ◽  
Systematic Literature Search ◽  
Random Effects Models ◽  
Unique Data ◽  
Early Childhood Research

Engagement in early childhood has been linked with later achievement, but the relation between these variables and how they are measured in early childhood requires examination. We estimated the overall association between academic engagement and achievement in children prior to kindergarten entry. Our systematic literature search yielded 13,521 reports for structured eligibility screening; from this pool of studies, we identified 21 unique data sets, with 199 effect sizes for analysis. We coded eligible studies, extracted effect sizes, accounted for effect size dependency, and used random-effects models to synthesize findings. The overall correlation between academic engagement and achievement was r = .24 (range: −.08 to −.71), and moderator analyses did not significantly predict the relation between the two constructs. This study aligns with previous research on this topic and examines issues related to these measures, their constraints, and applications as they pertain to early childhood research.

Food supplementation and abundance estimation in the white-footed mouse

2006 ◽  
Vol 84 (8) ◽  
pp. 1210-1215 ◽  
Author(s):  
Pei-Jen L. Shaner
Keyword(s):  
Population Dynamics ◽  
Food Availability ◽  
Robust Design ◽  
Meta Analysis ◽  
Design Model ◽  
Food Supplementation ◽  
Abundance Estimates ◽  
Abundance Indices ◽  
Consumer Population ◽  
White Footed Mouse

Food availability often drives consumer population dynamics. However, food availability may also influence capture probability, which if not accounted for may create bias in estimating consumer abundance and confound the effects of food availability on consumer population dynamics. This study compared two commonly used abundance indices (minimum number alive (MNA) and number of animals captured per night per grid) with an abundance estimator based on robust design model as applied to the white-footed mouse ( Peromyscus leucopus (Rafinesque, 1818)) in food supplementation experiments. MNA consistently generated abundance estimates similar to the robust design model, regardless of food supplementation. The number of animals captured per night per grid, however, consistently generated lower abundance estimates compared with MNA and the robust design model. Nevertheless, the correlations between abundance estimates from MNA, number of animals captured, and robust design model were not influenced by food supplementation. This study demonstrated that food supplementation is not likely to create bias among these different measures of abundance. Therefore, there is a great potential for conducting meta-analysis of food supplementation effect on consumer population dynamics (particularly in small mammals) across studies using different abundance indices and estimators.

scholarly journals Meta-Analysis Combining New and Existing Data Sets Confirms that the TERT–CLPTM1L Locus Influences Melanoma Risk

2012 ◽  
Vol 132 (2) ◽  
pp. 485-487 ◽  
Author(s):  
Matthew H. Law ◽  
Grant W. Montgomery ◽  
Kevin M. Brown ◽  
Nicholas G. Martin ◽  
Graham J. Mann ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Data Sets ◽  
Melanoma Risk ◽  
Existing Data

scholarly journals Indicators of AEI Applied to the Delaware Estuary

2002 ◽  
Vol 2 ◽  
pp. 169-189 ◽  
Author(s):  
Lawrence W. Barnthouse ◽  
Douglas G. Heimbuch ◽  
Vaughn C. Anthony ◽  
Ray W. Hilborn ◽  
Ransom A. Myers
Keyword(s):  
Meta Analysis ◽  
Juvenile Fish ◽  
Fish Populations ◽  
Weight Of Evidence ◽  
Data Sets ◽  
Delaware Estuary ◽  
Fish Stocks ◽  
Trends Analysis ◽  
Definition Of

We evaluated the impacts of entrainment and impingement at the Salem Generating Station on fish populations and communities in the Delaware Estuary. In the absence of an agreed-upon regulatory definition of “adverse environmental impact” (AEI), we developed three independent benchmarks of AEI based on observed or predicted changes that could threaten the sustainability of a population or the integrity of a community.Our benchmarks of AEI included: (1) disruption of the balanced indigenous community of fish in the vicinity of Salem (the “BIC” analysis); (2) a continued downward trend in the abundance of one or more susceptible fish species (the “Trends” analysis); and (3) occurrence of entrainment/impingement mortality sufficient, in combination with fishing mortality, to jeopardize the future sustainability of one or more populations (the “Stock Jeopardy” analysis).The BIC analysis utilized nearly 30 years of species presence/absence data collected in the immediate vicinity of Salem. The Trends analysis examined three independent data sets that document trends in the abundance of juvenile fish throughout the estuary over the past 20 years. The Stock Jeopardy analysis used two different assessment models to quantify potential long-term impacts of entrainment and impingement on susceptible fish populations. For one of these models, the compensatory capacities of the modeled species were quantified through meta-analysis of spawner-recruit data available for several hundred fish stocks.All three analyses indicated that the fish populations and communities of the Delaware Estuary are healthy and show no evidence of an adverse impact due to Salem. Although the specific models and analyses used at Salem are not applicable to every facility, we believe that a weight of evidence approach that evaluates multiple benchmarks of AEI using both retrospective and predictive methods is the best approach for assessing entrainment and impingement impacts at existing facilities.

scholarly journals Identification of Genes Expressed in Hyperpigmented Skin Using Meta-Analysis of Microarray Data Sets

2015 ◽  
Vol 135 (10) ◽  
pp. 2455-2463 ◽  
Author(s):  
Lanlan Yin ◽  
Sergio G. Coelho ◽  
Julio C. Valencia ◽  
Dominik Ebsen ◽  
Andre Mahns ◽  
...  
Keyword(s):  
Microarray Data ◽  
Meta Analysis ◽  
Data Sets

scholarly journals How Much Does Education Improve Intelligence? A Meta-Analysis

2018 ◽  
Vol 29 (8) ◽  
pp. 1358-1369 ◽  
Author(s):  
Stuart J. Ritchie ◽  
Elliot M. Tucker-Drob
Keyword(s):  
Cognitive Abilities ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Data Sets ◽  
Policy Changes ◽  
Compulsory Schooling ◽  
Beneficial Effects ◽  
Regression Discontinuity Designs ◽  
Intelligence Test Scores ◽  
Schooling Policy

Intelligence test scores and educational duration are positively correlated. This correlation could be interpreted in two ways: Students with greater propensity for intelligence go on to complete more education, or a longer education increases intelligence. We meta-analyzed three categories of quasiexperimental studies of educational effects on intelligence: those estimating education-intelligence associations after controlling for earlier intelligence, those using compulsory schooling policy changes as instrumental variables, and those using regression-discontinuity designs on school-entry age cutoffs. Across 142 effect sizes from 42 data sets involving over 600,000 participants, we found consistent evidence for beneficial effects of education on cognitive abilities of approximately 1 to 5 IQ points for an additional year of education. Moderator analyses indicated that the effects persisted across the life span and were present on all broad categories of cognitive ability studied. Education appears to be the most consistent, robust, and durable method yet to be identified for raising intelligence.

Positive Regard and Affirmation

2019 ◽  
pp. 288-322 ◽  
Author(s):  
Barry A. Farber ◽  
Jessica Y. Suzuki ◽  
David A. Lynch
Keyword(s):  
Treatment Outcome ◽  
Clinical Outcome ◽  
Meta Analysis ◽  
Positive Association ◽  
Data Sets ◽  
Unconditional Positive Regard ◽  
Use Of Data ◽  
Therapy Relationship ◽  
History Of ◽  
Repeated Use

This chapter meta-analytically reviews the research on the association between therapist positive regard and treatment outcome. The history of the construct of unconditional positive regard in client-centered theory, as well as the efforts to operationalize and measure this construct, are reviewed. Several clinical examples are presented. The meta-analysis, which features 64 studies, yielded a small positive association between positive regard and treatment outcome, g = 0.28. To control for the repeated use of data sets and study samples within the database, a multilevel meta-analysis was adopted that indicated a stronger relation between positive regard and clinical outcome, g = 0.36. These analyses support positive regard’s standing as a significant component of the therapy relationship that leads to improved clinical outcomes. The chapter concludes with limitations of the research, patient contributions, diversity considerations, and practice recommendations.

A systematical approach to classification problems with feature space heterogeneity

Kybernetes ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 2006-2029
Author(s):  
Hongshan Xiao ◽  
Yu Wang
Keyword(s):  
Factor Analysis ◽  
Meta Analysis ◽  
Feature Space ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Significant Feature ◽  
Data Sets ◽  
Data Set ◽  
Classification Techniques ◽  
Content Type

Purpose Feature space heterogeneity exists widely in various application fields of classification techniques, such as customs inspection decision, credit scoring and medical diagnosis. This paper aims to study the relationship between feature space heterogeneity and classification performance. Design/methodology/approach A measurement is first developed for measuring and identifying any significant heterogeneity that exists in the feature space of a data set. The main idea of this measurement is derived from a meta-analysis. For the data set with significant feature space heterogeneity, a classification algorithm based on factor analysis and clustering is proposed to learn the data patterns, which, in turn, are used for data classification. Findings The proposed approach has two main advantages over the previous methods. The first advantage lies in feature transform using orthogonal factor analysis, which results in new features without redundancy and irrelevance. The second advantage rests on samples partitioning to capture the feature space heterogeneity reflected by differences of factor scores. The validity and effectiveness of the proposed approach is verified on a number of benchmarking data sets. Research limitations/implications Measurement should be used to guide the heterogeneity elimination process, which is an interesting topic in future research. In addition, to develop a classification algorithm that enables scalable and incremental learning for large data sets with significant feature space heterogeneity is also an important issue. Practical implications Measuring and eliminating the feature space heterogeneity possibly existing in the data are important for accurate classification. This study provides a systematical approach to feature space heterogeneity measurement and elimination for better classification performance, which is favorable for applications of classification techniques in real-word problems. Originality/value A measurement based on meta-analysis for measuring and identifying any significant feature space heterogeneity in a classification problem is developed, and an ensemble classification framework is proposed to deal with the feature space heterogeneity and improve the classification accuracy.

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