The Experts below are selected from a list of 53307 Experts worldwide ranked by ideXlab platform
Gil Luria - One of the best experts on this subject based on the ideXlab platform.
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a Multilevel Model of safety climate cross level relationships between organization and group level climates
Journal of Applied Psychology, 2005Co-Authors: Dov Zohar, Gil LuriaAbstract:Technion—Israel Institute of Technology Organizational climates have been investigated separately at organization and subunit levels. This article tests a Multilevel Model of safety climate, covering both levels of analysis. Results indicate that organization-level and group-level climates are globally aligned, and the effect of organization climate on safety behavior is fully mediated by group climate level. However, the data also revealed meaningful group-level variation in a single organization, attributable to supervisory discretion in implementing formal procedures associated with competing demands like safety versus productivity. Variables that limit supervisory discretion (i.e., organization climate strength and procedural formalization) reduce both between-groups climate variation and within-group variability (i.e., increased group climate strength), although effect sizes were smaller than those associated with cross-level climate relationships. Implications for climate theory are discussed.
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a Multilevel Model of safety climate cross level relationships between organization and group level climates
Journal of Applied Psychology, 2005Co-Authors: Dov Zohar, Gil LuriaAbstract:Organizational climates have been investigated separately at organization and subunit levels. This article tests a Multilevel Model of safety climate, covering both levels of analysis. Results indicate that organization-level and group-level climates are globally aligned, and the effect of organization climate on safety behavior is fully mediated by group climate level. However, the data also revealed meaningful group-level variation in a single organization, attributable to supervisory discretion in implementing formal procedures associated with competing demands like safety versus productivity. Variables that limit supervisory discretion (i.e., organization climate strength and procedural formalization) reduce both between-groups climate variation and within-group variability (i.e., increased group climate strength), although effect sizes were smaller than those associated with cross-level climate relationships. Implications for climate theory are discussed.
Dov Zohar - One of the best experts on this subject based on the ideXlab platform.
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a Multilevel Model of safety climate cross level relationships between organization and group level climates
Journal of Applied Psychology, 2005Co-Authors: Dov Zohar, Gil LuriaAbstract:Technion—Israel Institute of Technology Organizational climates have been investigated separately at organization and subunit levels. This article tests a Multilevel Model of safety climate, covering both levels of analysis. Results indicate that organization-level and group-level climates are globally aligned, and the effect of organization climate on safety behavior is fully mediated by group climate level. However, the data also revealed meaningful group-level variation in a single organization, attributable to supervisory discretion in implementing formal procedures associated with competing demands like safety versus productivity. Variables that limit supervisory discretion (i.e., organization climate strength and procedural formalization) reduce both between-groups climate variation and within-group variability (i.e., increased group climate strength), although effect sizes were smaller than those associated with cross-level climate relationships. Implications for climate theory are discussed.
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a Multilevel Model of safety climate cross level relationships between organization and group level climates
Journal of Applied Psychology, 2005Co-Authors: Dov Zohar, Gil LuriaAbstract:Organizational climates have been investigated separately at organization and subunit levels. This article tests a Multilevel Model of safety climate, covering both levels of analysis. Results indicate that organization-level and group-level climates are globally aligned, and the effect of organization climate on safety behavior is fully mediated by group climate level. However, the data also revealed meaningful group-level variation in a single organization, attributable to supervisory discretion in implementing formal procedures associated with competing demands like safety versus productivity. Variables that limit supervisory discretion (i.e., organization climate strength and procedural formalization) reduce both between-groups climate variation and within-group variability (i.e., increased group climate strength), although effect sizes were smaller than those associated with cross-level climate relationships. Implications for climate theory are discussed.
Martin Cormican - One of the best experts on this subject based on the ideXlab platform.
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trimethoprim and ciprofloxacin resistance and prescribing in urinary tract infection associated with escherichia coli a Multilevel Model
Journal of Antimicrobial Chemotherapy, 2012Co-Authors: Akke Vellinga, Sana Tansey, Belinda Hanahoe, Kathleen Bennett, Andrew W Murphy, Martin CormicanAbstract:Objectives: Individual and group level factors associated with the probability of antimicrobial resistance of uropathogenic Escherichia coli were analysed in a Multilevel Model. Methods: Adult patients consulting with a suspected urinary tract infection (UTI) in 22 general practices over a 9 month period supplied a urine sample for laboratory analysis. Cases were patients with a UTI associated with a resistant E. coli. Previous antimicrobial exposure and other patient characteristics were recorded from the medical files. Results: Six hundred and thirty-three patients with an E. coli UTI and a full record for all variables were included. Of the E. coli isolates, 36% were resistant to trimethoprim and 12% to ciprofloxacin. A Multilevel logistic regression Model was fitted. The odds that E. coli was resistant increased with increasing number of prescriptions over the previous year for trimethoprim from 1.4 (0.8‐2.2) for one previous prescription to 4.7 (1.9‐12.4) for two and 6.4 (2.0‐25.4) for three or more. For ciprofloxacin the ORs were 2.7 (1.2‐5.6) for one and 6.5 (2.9‐ 14.8) for two or more. The probability that uropathogenic E. coli was resistant showed important variation between practices and a difference of 17% for trimethoprim and 33% for ciprofloxacin was observed for an imaginary patient moving from a practice with low to a practice with high probability. This difference could not be explained by practice prescribing or practice resistance levels. Conclusions: Previous antimicrobial use and the practice visited affect the risk that a patient with a UTI will be diagnosed with an E. coli resistant to this agent, which was particularly important for ciprofloxacin.
Jan De Leeuw - One of the best experts on this subject based on the ideXlab platform.
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A predictive density approach to predicting a future observable in Multilevel Models
Journal of Statistical Planning and Inference, 2005Co-Authors: David Afshartous, Jan De LeeuwAbstract:Abstract A predictive density function g ∗ is obtained for the Multilevel Model which is optimal in minimizing a criterion based on Kullback–Leibler divergence for a restricted class of predictive densities, thereby extending results for the normal linear Model (J. Amer. Statist. Assoc. 81 (1986) 196). Based upon this predictive density approach, three prediction methods are examined: Multilevel, prior, and OLS. The OLS prediction method corresponds to deriving a predictive density separately in each group, while the prior prediction method corresponds to deriving a predictive density for the entire Model. The Multilevel prediction method merely adjusts the prior prediction method by employing a well-known shrinkage estimator from Multilevel Model estimation. Multilevel data are simulated in order to assess the performance of these three methods. Both predictive intervals and predictive mean square error (PMSE) are used to assess the adequacy of prediction. The Multilevel prediction method outperforms the OLS and prior prediction methods, somewhat surprising since the OLS and prior prediction methods are derived from the Kullback–Leibler divergence criterion. This suggests that the restricted class of predictive densities suggested by Levy and Perng for the normal linear Model may need to be expanded for the Multilevel Model.
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CENTERING IN Multilevel ModelS
2004Co-Authors: Jan De LeeuwAbstract:This is an entry for The Encyclopedia of Statistics in Be- havioral Science, to be published by Wiley in 2005. Consider the situation in which we have m groups of individuals, where group j has n j members. We consider a general Multilevel Model, i.e. a random coefficient Model for each group of the form
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AN APPLICATION OF Multilevel Model PREDICTION TO NELS:88
Behaviormetrika, 2004Co-Authors: David Afshartous, Jan De LeeuwAbstract:Multilevel Modeling is often used in the social sciences for analyzing data that has a hierarchical structure, e.g., students nested within schools. In an earlier study, we investigated the performance of various prediction rules for predicting a future observable within a hierarchical data set (Afshartous & de Leeuw, 2004). We apply the Multilevel prediction approach to the NELS:88 educational data in order to assess the predictive performance on a real data set; four candidate Models are considered and predictions are evaluated via both cross-validation and bootstrapping methods. The goal is to develop Model selection criteria that assess the predictive ability of candidate Multilevel Models. We also introduce two plots that 1) aid in visualizing the amount to which the Multilevel Model predictions are “shrunk” or translated from the OLS predictions, and 2) help identify if certain groups exist for which the predictions are particularly good or bad.
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A Predictive Density Approach to Predicting a Future Observable in Multivariate Models
2002Co-Authors: David Afshartous, Jan De LeeuwAbstract:A predictive density function g' is obtained for the Multilevel Model which is optimal in minimizing a criterion based on Kullback-Leibler divergence for a restricted class of predictive densitites, thereby extending results for the normal linear Model (levy & Perng 1986). Based upon this predictive density approach three prediction methods are examined: Multilevel, Prior, and OLS. The OLS prediction method corresponds to deriving a predictive density separately in each group, while the Prior prediction method corresponds to deriving a predictive denstiy for the entire Model. The Multilevel prediction method merely adjusts the Prior prediction method by employing a well known shrinkage estimator from Multilevel Model estimation. Multilevel data is simulated in order to assess the preformance of these three methods. Both predictive inervals and predictive mean square error (PMSE) are used to assess the adequacy of prediction. The Multilevel prediction method outperforms the OLS and prior prediction methods some what surprising since the OLS and Prior prediction methods are derived from the Kullback-Leibler divergence criterion. This suggests that the restricted class of predctive densities suggest by Levy & Perng for the normal linear Model may need to be expanded for the Multilevel Model.
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An Application of Multilevel Model Prediction to NELS:88
2002Co-Authors: David Afshartous, Jan De LeeuwAbstract:Multilevel Modelling is often used in the social sciences for analyzing data that has a hiearchial structure, e.g.. students nesteded within schools. In an earlier study, we investigated the performance of various prediction rules for predicting a future observable within a hierachial data set (Afshartous & de Leeuw 2002). We apply the Multilevel prediction approach to the NELS:88 educational data in order to asses the improvement in prediction; the goal is to develop Model selction criteria (Multilevel cross-validation and Multilevel bootstrap) that assess the predictive ability of Multilevel Models. We also introduce two plots that 1) aid in the visualization the amount to which the Multilevel Model predictions are shrunk or translated from the OLS predictions, 2) help identify if certain groups exist for which the predictions are pariculary good or bad.
Neal M. Ashkanasy - One of the best experts on this subject based on the ideXlab platform.
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A Multilevel Model of affect and organizational commitment
Asia Pacific Journal of Management, 2010Co-Authors: Yan Li, David Ahlstrom, Neal M. AshkanasyAbstract:This Multilevel study investigates affective antecedents of organizational commitment. 230 individuals from 56 working groups were surveyed in eight mainland Chinese firms. The results showed that frequently experienced feelings of guilt and determination in organizations were positively related to increased organizational commitment. In addition, the increase of intragroup relationship conflict strengthened the negative association between chaotic emotions and organizational commitment. The findings suggest that the overall commitment to an organization is related to certain emotions in an organizational setting. This study, which employed a large sample from mainland China, proved consistent with past theory and empirical evidence from the West. A Multilevel Model of affective events theory with wide applicability is correspondingly proposed.
