The table below presents two different tests: the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s test of Sphericity.
KMO KMO is a test conducted to examine the strength of the partial correlation (how the factors explain each other) between the variables.
In a multiple regression model where both independent and dependent variables are continuous, one of the most common method for calculating the effect size of each of the variables or construct is Cohen’s f2.
Exogenous variables are also known as predictors/independent variables. They are those variables that cause changes in other variables (endogenous variable). Changes that occur in the exogenous variables are as a result of respondents’ demographical characteristics and hence cannot be explained by the model.
Common method bias is normally prevalent in studies where data for both independent and dependent variables are obtained from the same person in the same measurement context using the same item context and similar item characteristics.
In the model summary of your regression output, you see values of R, R Square, Adjusted R Square, R Square Change and F Change. This post will teach you the right way of interpretating them with good examples.
Last updated on
Mar 13, 2020
5 min read
Since a construct/latent variable is measured with multiple items, it is important to find the average of these items particularly when one wishes to conduct a multiple linear regression or maybe look out for the correlation between constructs.