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What are the correlates, causes and consequences?
Published in Wilmar Schaufeli, Dirk Enzmann, The Burnout Companion to Study and Practice: A Critical Analysis, 2020
Wilmar Schaufeli, Dirk Enzmann
In the next sections, we will review the outcomes of over 250 studies on burnout that can be grouped according to antecedents or possible causes (Section 4.2) and concomitants or possible consequences (Section 4.3). The majority of these studies are based on cross-sectional data so that causal relationships cannot be confirmed. Accordingly, most studies report on assumed or possible causes and consequences, which are, as a matter of fact, correlates of burnout. In our review, we supplement and contrast cross-sectional findings with outcomes from longitudinal studies. Special attention is paid in Section 4.4 to longitudinal studies that investigate the relationship between job demands and burnout. The reason for this is twofold: (1) the practical and theoretical importance of the job demands-burnout relationship; (2) the availability of a reasonable number of methodologically sound longitudinal studies.
Data Statistics and Analytics
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
Time series analysis is a statistical technique that represents time-series data or a trend analysis. This will represent the raw data in a graphical view. This method is used to analyze time-series data in order to extract meaningful information. Time is represented by the X-axis and the data series observations are represented by the Y-axis. ‘Time series’ means that the data is from a series of particular time periods or intervals. Time series, cross sectional, and pooled data are the types of data considered for the analysis. In time-series data, the data is from observations of a variable at different periods of time. Cross-sectional data represents multiple variables collected at the same point in time. Pooled data is a combination of time series and cross-sectional data. Time series forecasting is applied to predict future values based on values observed in the past. An interrupted time series is the analysis of interventions on a single time series. They are different from spatial data analysis where the observations typically relate to geographical locations. A stochastic model in a time series is more accurate when the observations are closer than when they are further apart. A time-series analysis can be applied to real-valued, continuous, and discrete numeric data. There are two solution techniques for time series: frequency domain and time-domain methods. Spectral and wavelet methods are the methods of solution followed in a frequency domain technique, whereas autocorrelation and cross-correlation are applied to the domain method. Figure 7.2 shows time series analysis with random data and a best-fit line.
Aviation Forecasting and Regression Analysis
Published in Bijan Vasigh, Ken Fleming, Thomas Tacker, Introduction to Air Transport Economics, 2018
Bijan Vasigh, Ken Fleming, Thomas Tacker
In contrast to qualitative methods, the quantitative methods use statistical data to analyze and forecast future behavior of specific variables. Statistical information is divided into time-series and cross-sectional data. Cross-sectional data are data compiled for different variables at a point in time; for example, the number of passengers over different geographically located airports, or the number of aviation accidents over different countries for one time period. Time series data, on the other hand, represent observations of a particular variable over a number of time periods—for example, the number of passengers at various past points in time at a given airport.
Attitudes towards gamification advertising in Vietnam: a social commerce context
Published in Behaviour & Information Technology, 2023
Hai Ho Nguyen, Bang Nguyen-Viet, Yen Thi Hoang Nguyen
This study evaluated the potential problems of multicollinearity and common method bias (CMB) using the VIF. In this regard, if the VIF value is less than 3.0, then multicollinearity and common method bias are not problematic (Hair et al. 2010). As the results show that all VIF values are less than 3.0 (from 1.176–1.581 for the constructs), it can be affirmed that the model is free of the problems of multicollinearity and CMB. Moreover, cross-sectional data have a propensity to produce misleading connections between variables, which may lead to common method bias (Fuller et al. 2016). Harman's single-factor test is the approach most often used in research to address the issue of common method bias (Podsakoff et al. 2003). A single-factor test was conducted, and the results indicated that no single factor accounted for the majority of the variance, and the first factor accounted for only 21.393% of the variance, which is <50% (Podsakoff et al. 2003). Consequently, no common method bias was identified in the data.
Identifying childhood movement profiles and comparing differences in mathematical skills between clusters: A latent profile analysis
Published in Journal of Sports Sciences, 2021
Timo Jaakkola, Airi Hakkarainen, Arto Gråsten, Elina Sipinen, Anssi Vanhala, Mikko Huhtiniemi, Anu Laine, Kasper Salin, Pirjo Aunio
This study has few limitations. First, we were not able to measure socioeconomic status of students’ families. Secondly, cross-sectional data does not allow us to draw causal conclusions. Thirdy, it should be recognised that “skilled movers” and “expert movers” profiles were rather small, which weakens the statistical power of the analyses. Lastly, one of the limitations is that we only measured maths performance. Future studies should also investigate if other academic domains (e.g., reading) have associations with students’ motor performance. In future studies, it would be beneficial to add open problems with multiple correct answers to ProbSol instrument to enhance students’ creativity. Additionally, previous studies have demonstrated that PA interventions have also contributed to academic performance34, including mathematical skills (Ericsson and Karlsson, 2014). In the future, these intervention studies should be targeted especially for students who have weak HRF and MC and investigate whether increases in PA engagement, and subsequently, physical performance have a positive association with BasicMath and ProbSol skills.
Statistical inference for partially linear errors-in-variables panel data models with fixed effects
Published in Systems Science & Control Engineering, 2021
Bangqiang He, Minxiu Yu, Jinming Zhou
Panel data records information on each individual unit over time, the rich information contained in panel data allows researchers to estimate complex models and answer questions that may not be possible using time series or cross-sectional data alone. Panel data analysis has received a lot of attention during the last two decades due to applications in many disciplines, such as economics, finance, biology, engineering and social sciences. Baltagi (2005) and Hsiao (2003) provided excellent overviews of statistical inference and econometric analysis of parametric panel data models. Semiparametric regression models reduce the high risk of misspecification relative to a fully parametric model and avoid some serious drawbacks of purely nonparametric methods such as the curse of dimensionality, difficulty of interpretation, and lack of extrapolation capability. In the last two decades, various forms of semiparametric regression models have been developed. These include the varying-coefficient model (e.g. Zhao & Lin, 2019), the partially linear regression model (e.g. Engle et al., 1986), the varying-coefficient partially linear model (e.g. J. Fan & Huang, 2005). The partially linear panel data models with fixed effects is a useful tool for econometric analysis; see, e.g. Henderson et al. (2008), Hu (2014), and Li et al. (2011).