Outliers or extreme value are numbers or values in a dataset that is often too big or too small when compared to the others. For example, suppose we want to determine the average monthly income of 15 employees in an organization with the expectation that the average income will be 1,000 USD. But we noticed […]
Before reading this post, we assume the reader has at least a basic understanding of discriminant validity as explained here. The Fronell-Larcker criterion is one of the most popular techniques used to check the discriminant validity of measurements models. According to this criterion, the square root of the average variance extracted by a construct must […]
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. Endogenous variables, on the other hand, are synonymous with the outcome/dependent variables. […]
In Structural Equation Modeling (SEM), measurement errors are those errors that occur in your items/questions in a questionnaire while measuring a latent variable. Each of these items is assigned a unique item error/measurement error to take care of the flaws or discrepancies that may have arisen in your measurement instruments. Residual Errors also called disturbance […]
This is another way a researcher can measure the internal consistency of his items. It is recommended that the reliability of a construct is at least 0.70. High composite reliability is a very good indication that all your items constantly measure the same construct. From the results below, we could see all the composite reliability […]
When a researcher finds the average variance extracted for a construct, he is interested in knowing, on average, how much variations in his items can be explained by the construct or latent variable. For instance: in the table below, perceived quality of information in Wikipedia (Qu) was measured with four items and the AVE for […]
Pause! Let me ask you a question, how reliable was the questionnaire you last distributed or how reliable were the various items/questions measuring their respective construct? Oh, maybe you are yet to distribute the questionnaires after the respondents have carefully filled them out, how reliable are those responses going be, what about the errors in […]
Confirmatory Factor Analysis (CFA) is conducted after the researcher has gained substantial knowledge about the latent variables (s)he wishes to study. Thus, the researcher has already explored the underlying latent variables and know exactly the number of items/indicators that loads strongly on each of them via Exploratory Factor Analysis (EFA). Unlike EFA, CFA aim at […]
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. That is, the higher the communality (closer to 1) the larger the amount of information that will be extracted. A good way to measure factor analysis is to have […]
In Structural Equation Modeling (SEM), we can think of a model in two perspectives: Measurement Model Structural Model The measurement model deals with the relationship between a latent variable and its indicators. For example: In measuring the ATTITUDE(ATT) of employees towards the ADOPTION INTENTION of new technology at the workplace, the researcher has four (4) […]
Missing value or data is the absence of a datapoint in a dataset. The problem of missing value occurs when a respondent fails to answer a question or a data entry problem on the part of the researcher or errors in the data collection process. Endeavour to always conduct a missing value analysis as part […]
Download the excel file and open it on your device. Select the number of available indicators (see figure below). For example, we have four items or indicators measuring perceived quality of information in Wikipedia (Qu1, Qu2, Qu3 and Qu5), so we selected 4 indicators as shown below. Copy your factor loadings and paste them in […]
Concepts can be easily understood when they are explained in a non-technical way with good examples. We explain what these terms mean using a very simple example. A researcher is interested in examining university faculty user behaviour of information in Wikipedia as a teaching resource using variables such as Quality, Perceived Ease of Use, and […]
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. Cohen categorized effect size as small, medium or large as shown in table 1. However, these conventions must be used carefully […]
