A range of analysis techniques which can examine quantitative data in more depth than can usually be obtained from a basic cross-analysis of the data by, for example, age, sex and social grade. The essence of this range of approaches is that the information is analysed in a way that permits patterns to emerge from within the data itself - ie based on the responses of the informants - rather than being imposed in advance, perhaps incorrectly or simplistically, by the researcher.
The study of two or more effects (“dependent variables”) at one time. – A measure of how many people or things in a group were studied by the researcher; followed by an equal sign and a numeral.
This refers to analysis of a sample (in this context, a protein) by more than one independent method of analysis. For example, a classical 2-D gel provides two independent, orthogonal parameters, pI and apparent size. MALDI of the intact protein from the gel provides a third dimension, mass, which is an intrinsic property of the protein and is similar, but more useful, than the relative size derived from the SDS dimension. MALDI of the CNBr fragments of the protein directly from the gel provides a true fourth dimension of analysis.
analysis of the relationships between three or more variables (as opposed to bivariate analysis, which involves two variables, or univariate analysis which involves one).
A statistical analysis technique in which multiple variables are analyzed separately to determine the contribution made by each variable to an observed result. Contrast with univariate analysis.
a statistical term refering to analyses that involve a number of different variables. For example, an analysis that looked at whether peer factors and individual factors both influence alcohol use would be called "multivariate".
A range of techniques that permit the analysis of several variables simultaneously.
Most often refers to statistical tests that examine more than two variables at the same time.
a generic term for any statistical technique used to analyze data from more than one variable
Any of several methods for examining multiple variables at the same time. Usage varies. (a) Stricter usage reserves the term for designs with two or more independent variables and two or more dependent variables. (b) More loosely, multivariate analysis applies to designs with more than one independent variable or more than one dependent variable or both. Whichever usage you prefer, either allows researchers to examine the relation between two variables while simultaneously controlling for the influence of other variables. Examples include path analysis, factor analysis, multiple regression analysis, MANOVA, LISREL, canonical correlations, and discriminant analysis.
A set of statistical techniques for analyzing the individual and joint effects of a number of factors on the outcomes of the process or disease being studied.
Multivariate analysis is a statistical technique that entails the analysis of the relationships between more than two variables. Carrying out multivariate analysis in this research means we could isolate the effect of design on business performance from other factors. See the Detailed research methodology for more information. Product and industrial design This design service covers design of any consumer /household products, furniture and industrial design such as automotive, engineering and medical products. R&D tax credits for SMEs Research and development (R&D) tax credits are a form of company tax relief which can either reduce a companyâ€(tm)s tax bill or, for some small or medium-sized companies, provide a cash sum. The aim of the tax credits is to encourage greater R&D spending in order to promote investment in innovation. See the HM Customs and Revenue website for more details.
the simultaneous study of two or more measures, often involving a relationship with a dependent (outcome) measure.
Analysis utilising more than one variable in combination, for example maintaining the wind and pressure fields in balance (Section 8.2.3).
The analysis of more than two variables simultaneously, for the purpose of determining the relationship among them.
a statistical analysis that involves more than one dependent variable.
analysis involving multiple independent or dependent variables.
The analysis of relationships between several variables – e.g. factor analysis.
is performed when datasets are composed of vector observations. These can consist of observations of different variables at one location or gridpoint values for a particular time sequence. Different multivariate data analysis techniques are used in climate research (see CCA, EOF, SVD, PCA, etc.). [3, pg 359] Top A-C D-H I-M N-R S-Z
A set of techniques used when variation in several variables has to be studied simultaneously.
Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which involves observation and analysis of more than one statistical variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest.
