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Nicholas M. Watanabe, Stephen Shapiro, and Joris Drayer

. Journal of the Association of Environmental and Resource Economists, 5 ( 4 ), 827 – 863 . doi:10.1086/698728 10.1086/698728 Bose , A. , Munir , A. , & Shabani , N. ( 2020 ). A quantitative analysis of big data clustering algorithms for market segmentation in the hospitality industry . In 2020

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Richard A. Preuss and Milos R. Popovic

This study defines the limits of stability in sitting, and quantitatively assesses two measures of postural control relative to these limits. Young, healthy subjects sat, feet unsupported, on an elevated force plate. The limits of stability were determined by a least square fit of an ellipse to the center of pressure (CoP) excursion during maximal leaning in 8 directions. These were highly symmetrical and centered within the base of support. The ellipses had a mean eccentricity of 0.66 (major axis in the sagittal plane) and covered an area approx. 1/3 of the base of support. The CoP was then monitored over 4 min of quiet sitting, during which the postural sway covered an area <0.05% of the limits of stability and was closely centered within the latter. Finally, target-directed trunk movements were performed, in 5 directions, at 4 movement speeds and 3 target distances. Increased target distance and movement speed both decreased the margin of stability (distance between the CoP and the limits of stability), as did movement in the frontal plane, reflecting the eccentricity of the limits of stability. These combined findings support the validity of this quantitative method of defining the limits of stability in sitting, for healthy individuals.

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Elaine A. Rose and Gaynor Parfitt

Using a mixed-method approach, the aim of this study was to explore affective responses to exercise at intensities below-lactate threshold (LT), at-LT, and above-LT to test the proposals of the dual-mode model (Ekkekakis, 2003). These intensities were also contrasted with a self-selected intensity. Further, the factors that influenced the generation of those affective responses were explored. Nineteen women completed 20 min of treadmill exercise at each intensity. Affective valence and activation were measured, pre-, during and postexercise. Afterward, participants were asked why they had felt the way they had during each intensity. Results supported hypotheses showing affect to be least positive during the above-LT condition and most positive during the self-selected and below-LT conditions. Individual differences were greatest in the below-LT and at-LT conditions. Qualitative results showed that factors relating to perceptions of ability, interpretation of exercise intensity, exercise outcomes, focus of concentration, and perceptions of control influenced the affective response and contributed to the individual differences shown in the quantitative data.

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K. Andrew R. Richards, Karen Lux Gaudreault, and Amelia Mays Woods

Purpose: This study sought to develop a quantitative understanding of factors that reduce perceived isolation and marginalization among physical educators. A conceptual model for the relationships among study variables was developed. Method: Data were collected through an online survey completed by 419 inservice physical educators (210 females, 209 males, 93.60% Caucasian). Variables included perceived mattering, resilience, personal accomplishment, as well as isolation and marginalization. Primary data analyses included structural equation modeling to test the hypothesized relationships in the conceptual model. Results: The structural equation model fit was good, χ2(315) = 669.38, p < .001, RMSEA = .05 (90% CI = [.05, .06], p = .285), SRMR = .05, NNFI = .93, CFI = .94. After removing non-significant regression pathways, the structural model generally confirmed the study hypotheses. Discussion/Conclusion: Enhancing personal accomplishment and resilience helps to foster perceptions of mattering, which reduces physical educators’ perceived isolation and marginalization.

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Stefano Palermi, Anna M. Sacco, Immacolata Belviso, Nastasia Marino, Francesco Gambardella, Carlo Loiacono, and Felice Sirico

the results. To the authors’ best knowledge, no previous qualitative or quantitative analysis summarizing these results has been carried out. Therefore, the aim of this systematic review and meta-analysis has been to systematically search and summarize the available scientific literature about the

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Linh Q. Vu, Rahul Agrawal, Mahdi Hassan, and Nils A. Hakansson

qualitative descriptions of human rolling. However, these studies have not examined the underlying neuromusculoskeletal activity associated with rolling. A quantitative analysis of human rolling could help aid in the identification of neuromusculoskeletal disorders that prohibit rolling oneself and provide

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Brian M. Moore, Joseph T. Adams, Sallie Willcox, and Joseph Nicholson

, and strength training) and outcome measures (assessment of postural responses while walking, using support surface translation, support surface tilt, cable release, and cable pull in standing) to support a quantitative analysis. Therefore, the studies are described and compared as a qualitative

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David Hortigüela-Alcalá, Antonio Calderón, and Gustavo González-Calvo

content analysis and constant comparison were used to assess the qualitative data. Questionnaires Within the quantitative analysis, a repeated-measures design was used. Analysis of variance was used for the independent groups. The analysis was performed by using the statistical package SPSS (version 22

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Samuel D. Muir, Sandun S.M. Silva, Mulu A. Woldegiorgis, Hayley Rider, Denny Meyer, and Madawa W. Jayawardana

WPAI targeting PA (eg, increase individuals’ daily step counts) or be a multicomponent WPAI that targeted PA in addition to some other health behavior(s) (eg, nutrition); (2) results of a quantitative analysis (eg, a logistic regression with odds ratios) were presented identifying predictors of

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Layne Case and Joonkoo Yun

were excluded, as the others did not reply with the data. One additional study ( Colebourn, Golub-Victor, & Paez, 2017 ) was excluded, as the sample size ( n  = 1) was too small to calculate the variance of the effect size. Thus, 18 articles were included in the quantitative analysis. Description of