scholarly journals On sufficient variable screening using log odds ratio filter

2022 ◽  
Vol 16 (1) ◽  
Author(s):  
Baoying Yang ◽  
Wenbo Wu ◽  
Xiangrong Yin
Keyword(s):  
Odds Ratio ◽  
Variable Screening ◽  
Log Odds

What do (some of) our association measures measure (most)? Association?

2021 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Corpus Linguistics ◽  
Odds Ratio ◽  
Association Measure ◽  
Measures Of Association ◽  
Association Measures ◽  
Log Odds ◽  
Dispersion Measures ◽  
Corpus Data ◽  
Behavior Supports ◽  
True Association

Abstract This paper discusses the degree to which some of the most widely-used measures of association in corpus linguistics are not particularly valid in the sense of actually measuring association rather than some amalgam of a lot of frequency and a little association. The paper demonstrates these issues on the basis of hypothetical and actual corpus data and outlines implications of the findings. I then outline how to design an association measure that only measures association and show that its behavior supports the use of the log odds ratio as a true association-only measure but separately from frequency; in addition, this paper sets the stage for an analogous review of dispersion measures in corpus linguistics.

Methods of Estimation in Log Odds Ratio Regression Models

Biometrics ◽  
1986 ◽  
Vol 42 (4) ◽  
pp. 949 ◽  
Author(s):  
N. E. Breslow ◽  
J. Cologne
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Log Odds

Residual Plots for Log Odds Ratio Regression Models

Biometrics ◽  
1991 ◽  
Vol 47 (3) ◽  
pp. 1135 ◽  
Author(s):  
Masaaki Tsujitani ◽  
Gary G. Koch
Keyword(s):  
Odds Ratio ◽  
Regression Models ◽  
Log Odds ◽  
Residual Plots

scholarly journals Bipolar disorder and cannabis use: a bidirectional two-sample Mendelian randomization study

2020 ◽  
Author(s):  
Oskar Hougaard Jefsen ◽  
Maria Speed ◽  
Doug Speed ◽  
Søren Dinesen Østergaard
Keyword(s):  
Bipolar Disorder ◽  
Standard Deviation ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Cannabis Use ◽  
Genome Wide ◽  
Log Odds ◽  
The Relationship ◽  
Mr Study

AbstractAimsCannabis use is associated with a number of psychiatric disorders, however the causal nature of these associations has been difficult to establish. Mendelian randomization (MR) offers a way to infer causality between exposures with known genetic predictors (genome-wide significant single nucleotide polymorphisms (SNPs)) and outcomes of interest. MR has previously been applied to investigate the relationship between lifetime cannabis use (having ever used cannabis) and schizophrenia, depression, and attention-deficit / hyperactivity disorder (ADHD), but not bipolar disorder, representing a gap in the literature.MethodsWe conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use. Genetic instruments (SNPs) were obtained from the summary statistics of recent large genome-wide association studies (GWAS). We conducted a two-sample bidirectional MR study on the relationship between bipolar disorder and lifetime cannabis use, using inverse-variance weighted regression, weighted median regression and Egger regression.ResultsGenetic liability to bipolar disorder was significantly associated with an increased risk of lifetime cannabis use: scaled log-odds ratio (standard deviation) = 0.0174 (0.039); P-value = 0.00001. Genetic liability to lifetime cannabis use showed no association with the risk of bipolar disorder: scaled log-odds ratio (standard deviation) = 0.168 (0.180); P-value = 0.351. The sensitivity analyses showed no evidence for pleiotropic effects.ConclusionsThe present study finds evidence for a causal effect of liability to bipolar disorder on the risk of using cannabis at least once. No evidence was found for a causal effect of liability to cannabis use on the risk of bipolar disorder. These findings add important new knowledge to the understanding of the complex relationship between cannabis use and psychiatric disorders.

scholarly journals Aplikasi Rasch Model dalam Mengevaluasi Intelligenz Structure Test (IST)

2018 ◽  
Vol 3 (1) ◽  
pp. 73
Author(s):  
Yulinda Erma Suryani
Keyword(s):  
Rasch Model ◽  
Odds Ratio ◽  
Linear Measure ◽  
Test Items ◽  
The Social ◽  
Log Odds ◽  
Proper Estimation ◽  
The Rasch Model ◽  
Made In

<p class="IABSTRAK"><strong>Abstract:</strong> The concept of objective measurement in the social sciences and educational assessment must have five criteria: 1) Gives a linear measure with the same interval; 2) Conduct a proper estimation process; 3) Finding unfeasible items (misfits) or outliers; 4) Overcoming the lost data; 5) Generate replicable measurements (independent of the parameters studied). These five conditions of measurement, so far only Rasch model that can fulfill it. The quality of intelligence measurements made with the Rasch model will have the same quality as the measurements made in the physical dimension in the field of physics. The logit scale (log odds unit) generated in the Rasch model is the scale of the same interval and is linear from the data ratio (odds ratio).  Based on the results of the analysis that has been done on the IST test instrument can be seen that in general the quality of IST test included in either category. Of the 176 IST test items there is only 1 item that is not good, ie aitem 155 (WU19) so that aitem 155 should be discarded. Based on the DIF analysis it can be seen that there are 28 items in favor of one gender only, so the twenty-eight items should be revised.</p><strong>Abstrak: </strong>Konsep pengukuran objektif dalam ilmu sosial dan penilaian pendidikan harus memiliki lima kriteria: 1) Memberikan ukuran yang linier dengan interval yang sama; 2) Melakukan proses estimasi yang tepat; 3) Menemukan item yang tidak tepat (misfits) atau tidak umum (outlier); 4) Mengatasi data yang hilang; 5) Hasilkan pengukuran yang <em>replicable </em>(independen dari parameter yang diteliti). Kelima kondisi pengukuran ini, sejauh ini hanya model Rasch yang bisa memenuhinya. Kualitas pengukuran kecerdasan yang dibuat dengan model Rasch akan memiliki kualitas yang sama dengan pengukuran yang dibuat dalam dimensi fisik di bidang fisika. Skala logit (<em>log odds unit</em>) yang dihasilkan dalam Rasch model adalah skala interval yang sama dan linear dari rasio data (<em>odds ratio</em>). Berdasarkan hasil analisis yang telah dilakukan pada instrumen tes IST dapat diketahui bahwa secara umum kualitas tes IST termasuk dalam kategori baik. Dari 176 item tes IST hanya ada 1 item yang tidak bagus, yaitu aitem 155 (WU19) sehingga aitem 155 harus dibuang. Berdasarkan analisis DIF dapat dilihat bahwa ada 28 item yang mendukung satu jenis kelamin saja, sehingga dua puluh delapan item harus direvisi.

scholarly journals Peer influence in adolescent drinking behavior: A meta-analysis of stochastic actor-based modeling studies

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250169
Author(s):  
Valeria Ivaniushina ◽  
Vera Titkova
Keyword(s):  
Peer Influence ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Drinking Behavior ◽  
Selection Effect ◽  
Parameter Estimates ◽  
Effective Prevention ◽  
Peer Selection ◽  
Influence Effect ◽  
Log Odds

Objectives To measure the effects of peer influence and peer selection on drinking behavior in adolescence through a rigorous statistical approach designed to unravel these interrelated processes. Methods We conducted systematic searches of electronic databases, thesis collections and conference proceedings to identify studies that used longitudinal network design and stochastic actor-oriented modeling to analyze drinking behavior in adolescents. Parameter estimates collected from individual studies were analyzed using multilevel random-effects models. Results We identified 26 articles eligible for meta-analysis. Meta-analyses for different specifications of the peer influence effect were conducted separately. The peer influence effect was positive for every specification: for average similarity (avSim) mean log odds ratio was 1.27 with 95% confidence interval [0.04; 2.49]; for total similarity (totSim) 0.46 (95% CI = [0.44; 0.48]), and for average alter (avAlt) 0.70 (95% CI = [-0.01; 1.41]). The peer selection effect (simX) was also positive: 0.46 (95% CI = [0.28; 0.63]). Conversion log odds ratio values to Cohen’s d gives estimates from 0.25 to 0.70, which is considered as medium to large effect. Conclusions Advances in methodology for social network analysis have made it possible to accurately estimate peer influence effects free from peer selection effects. More research is necessary to clarify the roles of age, gender, and individual susceptibility on the changing behavior of adolescents under the influence of their peers. Understanding the effects of peer influence should inform practitioners and policy makers to design and deliver more effective prevention programs.

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