Your main reason for conducting discriminant validity for your study will be to show how distinct an item or set of items is from others. In other words, you are interested in showing that items measuring different constructs or variables have poor relationships or low correlation exist between them. If you fail to test for […]
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. Sources of common method bias Independent and dependent variables are used with the same item. The presence of errors […]
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.
You can’t trust outliers and their effect maybe be minimal or larger than you think. Their presence can result in a false positive or nagtive result. Let me walk you through the fundamental steps to address this issue using box and whisker plot.
The sole aim of this tutorial is to teach you how to assess common method bias using Harman’s one-factor test for your study especially when the same measurement instrument is used to collect data for both dependent and independent variables.
P-value is also known as probability value and it ranges from 0 to 1. The interpretation of a p-value varies depending on what one is studying. For instance, while a small p-value less than 0.05 provide good evidence against the null hypotheses for ANOVA, t-test, Pearson’s correlation, regression analysis, etc., in some cases such as […]
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.
Let’s assume from the scenario illustrated here, that the average income of all the customers is $1,300/month. Given that this amount is calculated from the sample drawn from the population, it is called a Statistic or Sample Statistic.
The sole aim of this tutorial is to teach you how to assess common method bias using Harman’s one-factor test for your study especially when the same measurement instrument is used to collect data for both dependent and independent 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.
Sample size play a very important role in every research you undertake. That is why it is very essential you gather enough information about your subject. In statistics, a sample size of at least 30 is considered large enough, however, the larger the sample size, the more accurate information you can obtain and generalize your […]
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.
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.
In Exploratory Factor Analysis (EFA), the researcher has no prior intention of confirming the existence of any existing relationship prior to the analysis of the data, but instead, the researcher allows the various techniques, methods, procedures and rules to reveal the relationship in the data.
