Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the ...domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique's potential.
In 1948 the first randomized controlled trial was published by the English Medical Research Council in the British Medical Journal. Until then, observations had been uncontrolled. Initially, trials ...frequently did not confirm hypotheses to be tested. This phenomenon was attributed to little sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were being recognized and subsequently better accounted for. Such flaws of a mainly technical nature have been largely implemented and after 1970 led to trials being of significantly better quality than before. The past decade focused, in addition to technical aspects, on the need for circumspection in the planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial organs, including ethics committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. This third edition not only explains classical statistical analyses of clinical trials, but addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, meta-analyses, and provides a framework of the best statistical methods currently available for such purposes.
This text details the basics and the latest of densitometry. This edition, updated and expanded, covers new applications and includes new material on radiation safety and an entire appendix devoted ...to the recent ISCD Position Development Conference.
Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic ...tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh2) of a multiple regression model with one explanatory variable (Xh) as the model's response variable and the others (Xi i ≠ h) as its explanatory variables. The variance (σh2) of the regression coefficients constituting the final regression model are proportional to the VIF. Hence, an increase in Rh2 (strong multicollinearity) increases σh2. The larger σh2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.
Statistical mechanics relies on the maximization of entropy in a system at thermal equilibrium. However, an isolated quantum many-body system initialized in a pure state remains pure during ...Schrödinger evolution, and in this sense it has static, zero entropy. We experimentally studied the emergence of statistical mechanics in a quantum state and observed the fundamental role of quantum entanglement in facilitating this emergence. Microscopy of an evolving quantum system indicates that the full quantum state remains pure, whereas thermalization occurs on a local scale. We directly measured entanglement entropy, which assumes the role of the thermal entropy in thermalization. The entanglement creates local entropy that validates the use of statistical physics for local observables. Our measurements are consistent with the eigenstate thermalization hypothesis.
Statistical hypothesis testing compares the significance probability value and the significance level value to determine whether or not to reject the null hypothesis. This concludes "significant or ...not significant." However, since this process is a process of statistical hypothesis testing, the conclusion of "statistically significant or not statistically significant" is more appropriate than the conclusion of "significant or not significant." Also, in many studies, the significance level is set to 0.05 to compare with the significance probability value,
-value. If the
-value is less than 0.05, it is judged as "significant," and if the
-value is greater than 0.05, it is judged as "not significant." However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05. In a statistical hypothesis test, the conclusion depends on the setting of the significance level value, so the researcher must carefully set the significance level value. In this study, the stages of statistical hypothesis testing were examined in detail, and the exact conclusions accordingly and the contents that should be considered carefully when interpreting them were mentioned with emphasis on statistical hypothesis testing and significance level. In 11 original articles published in the
in 2022, the interpretation of hypothesis testing and the contents of the described conclusions were reviewed from the perspective of statistical hypothesis testing and significance level, and the content that I would like to be supplemented was mentioned.
Institutions of higher education are operating in an increasingly complex and competitive environment. This paper identifies contemporary challenges facing institutions of higher education worldwide ...and explores the potential of Big Data in addressing these challenges. The paper then outlines a number of opportunities and challenges associated with the implementation of Big Data in the context of higher education. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on Big Data.
This article considers identification, estimation, and model fit issues for models with contemporaneous and reciprocal effects. It explores how well the models work in practice using Monte Carlo ...studies as well as real-data examples. Furthermore, by using models that allow contemporaneous and reciprocal effects, the paper raises a fundamental question about current practice for cross-lagged panel modeling using models such as cross-lagged panel model (CLPM) or random intercept cross-lagged panel model (RI-CLPM): Can cross-lagged panel modeling be relied on to establish cross-lagged effects? The article concludes that the answer is no, a finding that has important ramifications for current practice. It is suggested that analysts should use additional models to probe the temporalities of the CLPM and RI-CLPM effects to see if these could be considered contemporaneous rather than lagged. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
The use of ambulatory assessments (AAs) as an approach to gather self-reported questionnaires or self-collected biochemical data is constantly increasing to investigate the experiences, states, and ...behaviors of individuals and their interaction with external situational factors during everyday life. It is often implicitly assumed that data from different sampling protocols can be used interchangeably, despite them assessing processes over different timescales in different intervals and at different occasions, which depending on the variables under study may result in fundamentally different dynamics. There are multiple temporal parameters to consider and while there is an abundance of sampling protocols that are applied regularly, to date, there is only limited empirical background on the influence different approaches may have on the data and findings. In this review, we aim to give an overview of commonly used types of AA in psychology, psychiatry, and biobehavioral research with a breakdown by temporal design parameters. Additionally, we discuss potential advantages and pitfalls associated with the various approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Increasingly, psychologists make use of modern configurational comparative methods (CCMs), such as qualitative comparative analysis (QCA) and coincidence analysis (CNA), to infer regularity-theoretic ...causal structures from psychological data. At the same time, existing CCMs remain unable to reveal such structures in the presence of complex effects. Given the strong emphasis configurational methodology generally puts on the notion of complex causation, and the ubiquity of multieffect problems in psychological research, such as multimorbidity and polypharmacy, this limitation is severe. In this article, we introduce psychologists to combinational regularity analysis (CORA)-a new member in the family of CCMs-with which regularity-theoretic causal structures that may include complex effects can be uncovered. To this end, CORA draws on algorithms originally developed in electrical engineering for the analysis of multioutput switching circuits, which regulate the behavior of electrical signals between a set of inputs and a set of outputs. After having situated CORA within the landscape of modern CCMs, we present its technical foundations. Subsequently, we demonstrate the method's analytical and graphical capabilities by means of artificial and empirical data. To facilitate familiarization, we use the concept of the "method game" to compare CORA with QCA and CNA. Through CORA, configurational analyses of complex effects come into the analytical reach of CCMs. CORA thus represents a useful addition to the methodological toolkit of psychologists who want to analyze their data from a configurational perspective. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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