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Industrial and organizational psychology "focuses the lens of psychological science on a key aspect of human life, namely, their work lives. In general, the goals of I-O psychology are to better understand and optimize the effectiveness, health, and well-being of both individuals and organizations." It is an applied discipline within psychology and is an international profession. I-O psychology is also known as occupational psychology in the United Kingdom, organisational psychology in Australia and New Zealand, and work and organizational (WO) psychology throughout Europe and Brazil. Industrial, work, and organizational (IWO) psychology is the broader, more global term for the science and profession.
Psychology is the study of mind and behavior in humans and non-humans. Psychology includes the study of conscious and unconscious phenomena, including feelings and thoughts. It is an academic discipline of immense scope, crossing the boundaries between the natural and social sciences. Psychologists seek an understanding of the emergent properties of brains, linking the discipline to neuroscience. As social scientists, psychologists aim to understand the behavior of individuals and groups.
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. It is thus a basic methodology of metascience. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature.
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An N of 1 trial (N=1) is a clinical trial in which a single patient is the entire trial, a single case study. A trial in which random allocation can be used to determine the order in which an experimental and a control intervention are given to a patient is an N of 1 randomized controlled trial. The order of experimental and control interventions can also be fixed by the researcher.
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
Time-series segmentation is a method of time-series analysis in which an input time-series is divided into a sequence of discrete segments in order to reveal the underlying properties of its source. A typical application of time-series segmentation is in speaker diarization, in which an audio signal is partitioned into several pieces according to who is speaking at what times. Algorithms based on change-point detection include sliding windows, bottom-up, and top-down methods. Probabilistic methods based on hidden Markov models have also proved useful in solving this problem.
JASP is a free and open-source program for statistical analysis supported by the University of Amsterdam. It is designed to be easy to use, and familiar to users of SPSS. It offers standard analysis procedures in both their classical and Bayesian form. JASP generally produces APA style results tables and plots to ease publication. It promotes open science via integration with the Open Science Framework and reproducibility by integrating the analysis settings into the results. The development of JASP is financially supported by several universities and research funds. As the JASP GUI is developed in C++ using Qt framework, some of the team left to make a notable fork which is Jamovi which has its GUI developed in JavaScript and HTML5.
References
- ↑ Ferron, John; Rendina‐Gobioff, Gianna (2005), "Interrupted Time Series Design", Encyclopedia of Statistics in Behavioral Science, American Cancer Society, doi:10.1002/0470013192.bsa312, ISBN 978-0-470-01319-9 , retrieved 2020-03-09
- 1 2 3 4 5 McDowall, David; McCleary, Richard; McCleary, Professor of Criminology Law & Society and Planning Policy & Design Richard; Meidinger, Errol; Jr, Richard A. Hay (August 1980). Interrupted Time Series Analysis. SAGE. pp. 5–6. ISBN 978-0-8039-1493-3.
{{cite book}}:|first3=has generic name (help) - ↑ Handbook of Psychology, Research Methods in Psychology, p. 582
- ↑ Bollen; et al. (2019). "The minute-scale dynamics of online emotions reveal the effects of affect labeling". Nature Human Behaviour. 3 (1): 92–100. doi:10.1038/s41562-018-0490-5. PMID 30932057. S2CID 56399577.
- ↑ Brodersen; et al. (2015). "Inferring causal impact using Bayesian structural time-series models". Annals of Applied Statistics. 9: 247–274. arXiv: 1506.00356 . doi:10.1214/14-AOAS788. S2CID 2879370 . Retrieved 21 March 2019.
- ↑ Li, Yang; Liu, Yanlan; Bohrer, Gil; Cai, Yongyang; Wilson, Aaron; Hu, Tongxi; Wang, Zhihao; Zhao, Kaiguang (2022). "Impacts of forest loss on local climate across the conterminous United States: Evidence from satellite time-series observation" (PDF). Science of the Total Environment. 802: 149651. Bibcode:2022ScTEn.802n9651L. doi:10.1016/j.scitotenv.2021.149651. PMID 34525747.
- ↑ Li, Yang; Zhao, Kaiguang; Hu, Tongxi; Zhang, Xuesong. "BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition". GitHub .
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