Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e., multivariate random variables. Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the problem being studied.
Psychological statistics is application of formulas, theorems, numbers and laws to psychology. Statistical methods for psychology include development and application statistical theory and methods for modeling psychological data. These methods include psychometrics, factor analysis, experimental designs, and Bayesian statistics. The article also discusses journals in the same field.
Chemometrics is the science of extracting information from chemical systems by data-driven means. Chemometrics is inherently interdisciplinary, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors other interdisciplinary fields, such as psychometrics and econometrics.
Herman Ole Andreas Wold was a Norwegian-born econometrician and statistician who had a long career in Sweden. Wold was known for his work in mathematical economics, in time series analysis, and in econometric statistics.
Partial least squares regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical.
Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself.
In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary.
In psychology, discriminant validity tests whether concepts or measurements that are not supposed to be related are actually unrelated.
In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct. As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research. CFA was first developed by Jöreskog (1969) and has built upon and replaced older methods of analyzing construct validity such as the MTMM Matrix as described in Campbell & Fiske (1959).
LISREL is a proprietary statistical software package used in structural equation modeling (SEM) for manifest and latent variables. It requires a "fairly high level of statistical sophistication".
Karl Gustav Jöreskog is a Swedish statistician. Jöreskog is a professor emeritus at Uppsala University, and a co-author of the LISREL statistical program. He is also a member of the Royal Swedish Academy of Sciences. Jöreskog received his bachelor's, master's, and doctoral degrees at Uppsala University. He is also a former student of Herman Wold. He was a statistician at Educational Testing Service (ETS) and a visiting professor at Princeton University.
The partial least squares path modeling or partial least squares structural equation modeling is a method for structural equation modeling that allows estimation of complex cause-effect relationships in path models with latent variables.
Christian M. Ringle is a professor of management at the Hamburg University of Technology, Germany. He is one of the co-founders and co-developers of SmartPLS, a Java-based software package for composite-based structural equation modeling using the partial least squares path modeling method. Since 2018, Christian Ringle has been included in the Clarivate Analytics' highly cited researchers list.
WarpPLS is a software with graphical user interface for variance-based and factor-based structural equation modeling (SEM) using the partial least squares and factor-based methods. The software can be used in empirical research to analyse collected data and test hypothesized relationships. Since it runs on the MATLAB Compiler Runtime, it does not require the MATLAB software development application to be installed; and can be installed and used on various operating systems in addition to Windows, with virtual installations.
In statistics (classical test theory), average variance extracted (AVE) is a measure of the amount of variance that is captured by a construct in relation to the amount of variance due to measurement error.
In statistics, confirmatory composite analysis (CCA) is a sub-type of structural equation modeling (SEM). Although, historically, CCA emerged from a re-orientation and re-start of partial least squares path modeling (PLS-PM), it has become an independent approach and the two should not be confused. In many ways it is similar to, but also quite distinct from confirmatory factor analysis (CFA). It shares with CFA the process of model specification, model identification, model estimation, and model assessment. However, in contrast to CFA which always assumes the existence of latent variables, in CCA all variables can be observable, with their interrelationships expressed in terms of composites, i.e., linear compounds of subsets of the variables. The composites are treated as the fundamental objects and path diagrams can be used to illustrate their relationships. This makes CCA particularly useful for disciplines examining theoretical concepts that are designed to attain certain goals, so-called artifacts, and their interplay with theoretical concepts of behavioral sciences.
Marko Sarstedt is a German academic and a marketing researcher. He is a Full Professor at the Ludwig Maximilian University of Munich and Adjunct Research Professor at Babeș-Bolyai-University.
Necessary Condition Analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result. Illustratively, the admission of a student into a Ph.D. program necessitates an adequate GMAT score; the progression of AIDS mandates the presence of HIV; and the realization of organizational change will not occur without the commitment of management. Singular in nature, these conditions possess the potential to function as bottlenecks for the desired outcome. Their absence unequivocally guarantees the failure of the intended objective, a deficiency that cannot be offset by the influence of other contributing factors. It is noteworthy, however, that the mere presence of the necessary condition does not ensure the assured attainment of success. In such instances, the condition demonstrates its necessity but lacks sufficiency. To obviate the risk of failure, the simultaneous satisfaction of each distinct necessary condition is imperative. NCA serves as a systematic mechanism, furnishing the rationale and methodological apparatus requisite for the identification and assessment of necessary conditions within extant or novel datasets. It is a powerful method for investigating causal relationships and determining the minimum requirements that must be present for an outcome to be achieved.
