Statistical software programs calculate path coefficients
Differentiate between Univariate, Bivariate and Multivariate
Univariate, bivariate and multivariate are the various types of data that are based on the number of variables. Variables mean the number of objects that are under consideration as a sample in an experiment. Usually there are three types of data sets. These are;
Multivariate Data Analysis:
Multivariate data is the data in which analysis are based on more than two variables per observation. Usually multivariate data is used for explanatory purposes.
After gathering the data for the particular variable in question a researcher can then determine a
number of measures regarding the distribution of the data, including: the median, mean, standard
variables (often denoted as X,Y), for the purpose of determining the empirical relationship between
them.
regression).
Bivariate analysis can be contrasted with univariate analysis in which only one variable is
The Bivariate platform shows the relationship between two continuous variables. It is the continuous by continuous personality of the Fit Y by X platform. The word bivariate simply means involving two variables instead of one (univariate) or many (multivariate).
The Bivariate analysis results appear in a scatterplot. Each point on
the plot represents the X and Y scores for a single subject; in other
words, each point represents two variables. Using the
scatterplot, you can see at a glance the degree and pattern of the
relationship between the two variables. You can interactively add other
types of fits, such as simple linear regression, polynomial regression,
and so on.
Types
There are many statistical techniques for conducting multivariate analysis, and the most appropriate technique for a given study varies with the type of study and the key research questions. Four of the most common multivariate techniques are multiple regression analysis, factor analysis, path analysis and multiple analysis of variance, or MANOVA.Multiple Regression
Multiple regression analysis, often referred to simply as regression analysis, examines the effects of multiple independent variables (predictors) on the value of a dependent variable, or outcome.
MANOVA
Multiple Analysis of Variance, or MANOVA, is an advanced form of the
more basic analysis of variance, or ANOVA. MANOVA extends the technique
to studies with two or more related dependent variables while
controlling for the correlations among them. An example of a study for
which MANOVA would be an appropriate technique is a study of health
among three groups of teens: those who exercise regularly, those who
exercise on occasion, and those who never exercise. A MANOVA for this
study would allow multiple health-related outcome measures such as
weight, heart rate, and respiratory rates.
Benefits
Multivariate statistical analysis is especially important in social
science research because
researchers in these fields are often unable to use randomized
laboratory experiments that their counterparts in medicine and natural
sciences often use. Instead, many social scientists must rely on
quasi-experimental designs in which the experimental and control groups
may have initial differences that could affect or bias the outcome of
the study. Multivariate techniques try to statistically account for
these differences and adjust outcome measures to control for the portion
that can be attributed to the differences.
