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.

Did you know that in STructural Equation Modeling (SEM) there are unique error terms associated with both measurement and structural model?

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.

When we talk about communalities, we are interested in the amount of original information contained in each variable that can be extracted from a common factor.

In exploratory factor analysis (EFA) , a scree plot is a plot of eigenvalues of factors arranged in descending order of magnitude from the left to the right side of the plot.

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.

A confidence interval is a type of statistical interval computed from observed data. A confidence interval consists of a confidence level expressed in percent.

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.

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.

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