Measuring Anxiety in Athletics: The Revised Competitive State Anxiety Inventory–2

2003 ◽  
Vol 25 (4) ◽  
pp. 519-533 ◽  
Author(s):  
Richard H. Cox ◽  
Matthew P. Martens ◽  
William D. Russell
Keyword(s):  
State Anxiety ◽  
Division I ◽  
Validation Sample ◽  
Lagrange Multiplier Test ◽  
Validation Data ◽  
Data Set ◽  
College Age ◽  
Calibration Sample ◽  
Confirmatory Factor ◽  
Interscholastic Athletes

The purpose of this study was to use confirmatory factor analysis (CFA) to revise the factor structure of the CSAI-2 using one data set, and then to use CFA to validate the revised structure using a second data set. The first data set (calibration sample) consisted of 503 college-age intramural athletes, and the second (validation sample) consisted of 331 intercollegiate (Division I) and interscholastic athletes. The results of the initial CFA on the calibration sample resulted in a poor fit to the data. Using the Lagrange Multiplier Test (Gamma) as a guide, CSAI-2 items that loaded on more than one factor were sequentially deleted. The resulting 17-item revised CSAI-2 was then subjected to a CFA using the validation data sample. The results of this CFA revealed a good fit of the data to the model (CFI = .95, NNFI = .94, RMSEA = .054). It is suggested that the CSAI-2R instead of the CSAI-2 be used by researchers and practitioners for measuring competitive state anxiety in athletes.

scholarly journals An Investigation of the Factor Structure of the 16PF, Version 5

2014 ◽  
Vol 35 (1) ◽  
pp. 38-46 ◽  
Author(s):  
Paul Irwing ◽  
Tom Booth ◽  
Mark Batey
Keyword(s):  
Factor Structure ◽  
Self Control ◽  
Model Fit ◽  
Validation Sample ◽  
Level Data ◽  
Calibration Sample ◽  
Confirmatory Factor ◽  
Item Level ◽  
The Us ◽  
Order Factor

In order to examine its higher-order factor structure, we applied confirmatory factor and invariance analysis to item level data from the US standardization sample of the 16PF5, divided into a calibration sample (N = 5,130) and a validation sample (N = 5,131). Using standard assessments of model fit, all primary factors displayed good to excellent model fit, thus suggesting the scales to be broadly unidimensional. Results indicated a drop in model fit in both the structural and configurally invariant second order models, suggesting some level of misspecification in the global scales of Extraversion, Anxiety, Tough-Mindedness, Independence, and Self-Control. However, the degree of misspecification was slight. Overall, the analyses generally supported the proposed structure of the 16PF5.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

scholarly journals Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e040778
Author(s):  
Vineet Kumar Kamal ◽  
Ravindra Mohan Pandey ◽  
Deepak Agrawal
Keyword(s):  
Hospital Mortality ◽  
External Validation ◽  
Trauma Centre ◽  
Unfavourable Outcome ◽  
Motor Score ◽  
Validation Data ◽  
Data Set ◽  
Development Data ◽  
Level 1 ◽  
Pupillary Reactivity

ObjectiveTo develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).DesignRetrospective.SettingLevel-1, government-funded trauma centre, India.ParticipantsPatients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.Outcome(s)In-hospital mortality and unfavourable outcome at 6 months.ResultsA total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).ConclusionFor clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

Evaluation of the CONSUME and FOFEM fuel consumption models in pine and mixed hardwood forests of the eastern United States

2014 ◽  
Vol 44 (7) ◽  
pp. 784-795 ◽  
Author(s):  
Susan J. Prichard ◽  
Eva C. Karau ◽  
Roger D. Ottmar ◽  
Maureen C. Kennedy ◽  
James B. Cronan ◽  
...  
Keyword(s):  
United States ◽  
Fuel Consumption ◽  
Prescribed Burning ◽  
Forest Type ◽  
Fire Effects ◽  
Eastern United States ◽  
Validation Data ◽  
Data Set ◽  
Predicted Values ◽  
Fine Fuels

Reliable predictions of fuel consumption are critical in the eastern United States (US), where prescribed burning is frequently applied to forests and air quality is of increasing concern. CONSUME and the First Order Fire Effects Model (FOFEM), predictive models developed to estimate fuel consumption and emissions from wildland fires, have not been systematically evaluated for application in the eastern US using the same validation data set. In this study, we compiled a fuel consumption data set from 54 operational prescribed fires (43 pine and 11 mixed hardwood sites) to assess each model’s uncertainties and application limits. Regions of indifference between measured and predicted values by fuel category and forest type represent the potential error that modelers could incur in estimating fuel consumption by category. Overall, FOFEM predictions have narrower regions of indifference than CONSUME and suggest better correspondence between measured and predicted consumption. However, both models offer reliable predictions of live fuel (shrubs and herbaceous vegetation) and 1 h fine fuels. Results suggest that CONSUME and FOFEM can be improved in their predictive capability for woody fuel, litter, and duff consumption for eastern US forests. Because of their high biomass and potential smoke management problems, refining estimates of litter and duff consumption is of particular importance.

scholarly journals 127 Predicting Dry Matter Intake of Gestating and Lactating Beef Cows

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 58-58
Author(s):  
Megan A Gross ◽  
Claire Andresen ◽  
Amanda Holder ◽  
Alexi Moehlenpah ◽  
Carla Goad ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Milk Yield ◽  
Feed Intake ◽  
Beef Cows ◽  
Multiple Regression Equation ◽  
Validation Data ◽  
Data Set ◽  
Lactating Cows ◽  
Downward Bias ◽  
Protein Supply

Abstract In 1996, the NASEM beef cattle committee developed and published an equation to estimate cow feed intake using results from studies conducted or published between 1979 and 1993 (Nutrient Requirements of Beef Cattle). The same equation was recommended for use in the most recent version of this publication (2016). The equation is sensitive to cow weight, diet digestibility and milk yield. Our objective was to validate the accuracy of this equation using more recent published and unpublished data. Criteria for inclusion in the validation data set included projects conducted or published within the last ten years, direct measurement of forage intake, adequate protein supply, and pen feeding (no tie stall or metabolism crate data). The validation data set included 29 treatment means for gestating cows and 26 treatment means for lactating cows. Means for the gestating cow data set was 11.4 ± 1.9 kg DMI, 599 ± 77 kg BW, 1.24 ± 0.14 Mcal/kg NEm per kg of feed and lactating cow data set was 14.5 ± 2.0 kg DMI, 532 ± 116.3 kg BW, and 1.26 ± 0.24 Mcal NEm per kg feed, respectively. Non intercept models were used to determine equation accuracy in predicting validation data set DMI. The slope for linear bias in the NASEM gestation equation did not differ from 1 (P = 0.07) with a 3.5% positive bias. However, when the NASEM equation was used to predict DMI in lactating cows, the slope for linear bias significantly differed from 1 (P < 0.001) with a downward bias of 13.7%. Therefore, a new multiple regression equation was developed from the validation data set: DMI= (-4.336 + (0.086427 (BW^.75) + 0.3 (Milk yield)+6.005785(NEm)), (R-squared=0.84). The NASEM equation for gestating beef cows was reasonably accurate while the lactation equation underestimated feed intake.

Abstract P192: Metabolomic Profiles Associated with Coronary Heart Disease in Women

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Nina P Paynter ◽  
Raji Balasubramanian ◽  
Shuba Gopal ◽  
Franco Giulianini ◽  
Leslie Tinker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Tier 2 ◽  
Validation Data ◽  
Data Set ◽  
Chd Risk ◽  
Tier 1 ◽  
Chd Risk Factors

Background: Prior studies of metabolomic profiles and coronary heart disease (CHD) have been limited by relatively small case numbers and scant data in women. Methods: The discovery set examined 371 metabolites in 400 confirmed, incident CHD cases and 400 controls (frequency matched on age, race/ethnicity, hysterectomy status and time of enrollment) in the Women’s Health Initiative Observational Study (WHI-OS). All selected metabolites were validated in a separate set of 394 cases and 397 matched controls drawn from the placebo arms of the WHI Hormone Therapy trials and the WHI-OS. Discovery used 4 methods: false-discovery rate (FDR) adjusted logistic regression for individual metabolites, permutation corrected least absolute shrinkage and selection operator (LASSO) algorithms, sparse partial least squares discriminant analysis (PLS-DA) algorithms, and random forest algorithms. Each method was performed with matching factors only and with matching plus both medication use (aspirin, statins, anti-diabetics and anti-hypertensives) and traditional CHD risk factors (smoking, systolic blood pressure, diabetes, total and HDL cholesterol). Replication in the validation set was defined as a logistic regression coefficient of p<0.05 for the metabolites selected by 3 or 4 methods (tier 1), or a FDR adjusted p<0.05 for metabolites selected by only 1 or 2 methods (tier 2). Results: Sixty-seven metabolites were selected in the discovery data set (30 tier 1 and 37 tier 2). Twenty-six successfully replicated in the validation data set (21 tier 1 and 5 tier 2), with 25 significant with adjusting for matching factors only and 11 significant after additionally adjusting for medications and CHD risk factors. Validated metabolites included amino acids, sugars, nucleosides, eicosanoids, plasmologens, polyunsaturated phospholipids and highly saturated triglycerides. These include novel metabolites as well as metabolites such as glutamate/glutamine, which have been shown in other populations. Conclusions: Multiple metabolites in important physiological pathways with robust associations for risk of CHD in women were identified and replicated. These results may offer insights into biological mechanisms of CHD as well as identify potential markers of risk.

scholarly journals Detection and Quantification of Cocoa Butter Equivalents in Cocoa Butter and Plain Chocolate by Gas Liquid Chromatography of Triacylglycerols

2004 ◽  
Vol 87 (5) ◽  
pp. 1153-1163 ◽  
Author(s):  
Manuela Buchgraber ◽  
Chiara Senaldi ◽  
Franz Ulberth ◽  
Elke Anklam
Keyword(s):  
Liquid Chromatography ◽  
Cocoa Butter ◽  
Fat Content ◽  
Average Error ◽  
Gas Liquid Chromatography ◽  
Cocoa Butter Equivalent ◽  
Validation Data ◽  
Data Set ◽  
Cocoa Butter Equivalents ◽  
Detection And Quantification

Abstract The development and in-house testing of a method for the detection and quantification of cocoa butter equivalents in cocoa butter and plain chocolate is described. A database consisting of the triacylglycerol profile of 74 genuine cocoa butter and 75 cocoa butter equivalent samples obtained by high-resolution capillary gas liquid chromatography was created, using a certified cocoa butter reference material (IRMM-801) for calibration purposes. Based on these data, a large number of cocoa butter/cocoa butter equivalent mixtures were arithmetically simulated. By subjecting the data set to various statistical tools, reliable models for both detection (univariate regression model) and quantification (multivariate model) were elaborated. Validation data sets consisting of a large number of samples (n = 4050 for detection, n = 1050 for quantification) were used to test the models. Excluding pure illipé fat samples from the data set, the detection limit was determined between 1 and 3% foreign fat in cocoa butter. Recalculated for a chocolate with a fat content of 30%, these figures are equal to 0.3–0.9% cocoa butter equivalent. For quantification, the average error for prediction was estimated to be 1.1% cocoa butter equivalent in cocoa butter, without prior knowledge of the materials used in the blend corresponding to 0.3% in chocolate (fat content 30%). The advantage of the approach is that by using IRMM-801 for calibration, the established mathematical decision rules can be transferred to every testing laboratory.

Evaluation of Kinetic Mechanisms for Direct Fired Supercritical Oxy-Combustion of Natural Gas

2016 ◽  
Author(s):  
Shane Coogan ◽  
Xiang Gao ◽  
Aaron McClung ◽  
Wenting Sun
Keyword(s):  
Carbon Dioxide ◽  
Natural Gas ◽  
San Diego ◽  
Laminar Flame ◽  
Flame Speed ◽  
Laminar Flame Speed ◽  
Validation Data ◽  
Data Set ◽  
Reduced Mechanism ◽  
Kinetic Mechanisms

Existing kinetic mechanisms for natural gas combustion are not validated under supercritical oxy-fuel conditions because of the lack of experimental validation data. Our studies show that different mechanisms have different predictions under supercritical oxy-fuel conditions. Therefore, preliminary designers may experience difficulties when selecting a mechanism for a numerical model. This paper evaluates the performance of existing chemical kinetic mechanisms and produces a reduced mechanism for preliminary designers based on the results of the evaluation. Specifically, the mechanisms considered were GRI-Mech 3.0, USC-II, San Diego 204-10-04, NUIG-I, and NUIG-III. The set of mechanisms was modeled in Cantera and compared against the literature data closest to the application range. The high pressure data set included autoignition delay time in nitrogen and argon diluents up to 85 atm and laminar flame speed in helium diluent up to 60 atm. The high carbon dioxide data set included laminar flame speed with 70% carbon dioxide diluent and the carbon monoxide species profile in an isothermal reactor with up to 95% carbon dioxide diluent. All mechanisms performed adequately against at least one dataset. Among the evaluated mechanisms, USC-II has the best overall performance and is preferred over the other mechanisms for use in the preliminary design of supercritical oxy-combustors. This is a significant distinction; USC-II predicts slower kinetics than GRI-Mech 3.0 and San Diego 2014 at the combustor conditions expected in a recompression cycle. Finally, the global pathway selection method was used to reduce the USC-II model from 111 species, 784 reactions to a 27 species, 150 reactions mechanism. Performance of the reduced mechanism was verified against USC-II over the range relevant for high inlet temperature supercritical oxy-combustion.

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